Given my journey, you can imagine my first reaction to questions of work-life balance is fairly unsympathetic. I want to protest that, by legitimizing such a false dichotomy, you’re pre-empting a much more meaningful conversation. But I suspect that conversation is closer to the heart of this anxiety than most people realize.

If you’re worrying about work-life balance at the beginning of your career, and you’re reading this, I’m guessing you’re not lazy. You’re not looking for an easy life (even if this seems like an appealing concept right after midterms). I’m willing to bet that what you’re really worried about is someone else owning your most precious possession: your future.

Staring into the abyss of companies that glorify triple-digit hours (never mind the substance of the work), this makes intuitive sense. But having surveyed the landscape of high-tech hiring, I’m convinced you should be just as concerned about jobs that promise high stimulation and total comfort. When you let yourself be sold on easy hours, outrageous perks, and glib assurances about the project you’ll join and the technologies you’ll get to play with, you’ve just agreed to let your future become someone else’s.

I hate the construct of work-life balance for the same reason I love engineering: the reality is dynamic and generative, not zero-sum. It’s about transcending the constraints of simplistic calculations. Creating the life and the work you want are by no means easy challenges, but they are absolutely attainable. What’s not realistic is thinking you can own your future and be comfortable at the same time. Grit, not virtuosity, will be the biggest determinant of your success, for reasons I’ll explore in a bit.

At the same time, grit and discipline aren’t enough. You need purpose. And I can state categorically that the purpose you discover, with all the sacrifice that entails, will be more motivating and meaningful than the one handed to you in the form of some glamorous project that, realistically, will succeed or fail regardless of your involvement.

The catch, of course, is that true purpose doesn’t sit around waiting to be discovered. It requires constant pursuit. Here’s what I’ve learned from a decade and a half of sprinting.

There’s no time like now. As learning animals, we’re subject to various ages of cognitive potency. As a young child, your aptitude for acquiring a language or learning an instrument is at its peak. Accordingly, as a professional, your early 20s are the most formative stage. It is absolutely critical to make the most of this time because the pace of learning grows slower and more incremental as you age, whether we care to admit it or not. Of course, you can always learn new things, but most often the wisdom of experience is largely the result of earlier realizations having the time to compound into something richer.

The place of maximal learning is often at the point of significant pain. It’s not just about having a more pliable mind - grit, and its close cousin, resilience, are essential for taking your intelligence further than it can get on its own. And while intelligence compounds, grit degrades in the vast majority of cases. Regardless, grit isn’t something you can suddenly develop after a life of leisure. For these reasons, owning your future means choosing grit over the allure of a predictable pace.

Of course, you still need to hold a pace. Studies show that marathoners/endurance runners do tons of self-talk to push past the pain. “It’s a marathon, not a sprint” is a well-worn cliché, but it’s striking how often it’s invoked to rationalize comfort as opposed to promoting sustained excellence. Don’t think for a second that elite marathoners have trained to the point that a sub-six-minute mile pace is comfortable. It’s incredibly painful. What separates the truly elite is having found a purpose that makes the sacrifice acceptable.

At the same time, complete self-motivation is incredibly rare. It’s probably not a realistic goal, and that’s fine. Find the people who will sharpen your resolve as well as your ideas. Again, your first step matters. If you choose a job for work-life balance, chances are, so did everyone who came before. Talent is one thing when evaluating your future teammates, but ask yourself this: when you need models and inspiration to be more than you are, will you be able to find them? Where will your gamma radiation come from?

You can find your zen in stressful, chaotic times. In fact, I’d argue this is the norm, even the ideal, for 20-somethings. Some adrenaline is good for your performance. Not having time to waste requires you to focus on the essentials and develop an innate sense of direction. That way, when you do eventually get to let your mind wander, it will be in rewarding directions. These days, I build in calendar blocks for “brain space”. That wouldn’t have made sense 10 or even 5 years ago – not because I have more free time now, but because, early in your career, you learn much more by doing than reflecting. And this can be the difference between creating your future and receiving it in a fancy envelope.

At the limit, you probably should care about work-life balance – it’s not going to remain a static thing your whole life. But at the margin, as a new grad, you should focus on the most important problem. Find the thing that motivates you, work your ass off, learn as much as you can, and trust that today’s gains will compound well into the future – your future.

Working your ass off isn’t bleak – it’s quite the opposite. Provided there’s a purpose, sprinting at an unsustainable pace is an act of tremendous optimism. A mindset of premature retirement might sound rosy, but in truth it’s deeply cynical and extraordinarily insidious – much more so than being overpaid or overpraised, and much harder to correct.

But back to the concept of caring about work-life balance at the limit, how do you know where the limit is? Isn’t life fundamentally uncertain? Here’s what I’ve come to realize: you can’t pre-emptively retire without doing the work that makes you appreciate the chance to rest. Maybe you can, but assuming you have something to contribute, it’s going to be an empty reward. Sacrificing your potential to comfort isn’t a hedge against an early death – it IS an early death. As Emerson wrote in Self-Reliance, "Life only avails, not the having lived. Power ceases in the instant of repose; it resides in the moment of transition from a past to a new state, in the shooting of the gulf, in the darting to an aim.”

We’ve been told over and over to choose life over work in order to achieve balance. I’m urging you, especially at the dawn of your career, to instead choose life over balance, and make the work so meaningful that you wouldn’t want it to exist as a distinct concept. This is how you ensure that your future remains yours.

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tag:shyamsankar.com,2013:Post/9217592015-10-24T22:13:41Z2016-09-26T13:05:13ZGamma Radiation: The Incredible Hulk As a Model for Personal Growth

If I've learned one thing from observing great individuals (and great companies), it's that greatness is inherently asymmetric. If that sounds dangerous, it is. Any scholar of counterterrorism or cyber war will tell you that asymmetric threats require asymmetric countermeasures, but more fundamentally, they require asymmetric people. When forming a team, I don't want to assemble a polite roster of cross-functional professionals. I want the X-Men: a medley of mutants united for good.

The Incredible Hulk, in particular, embodies the growth model I've come to believe is necessary for achieving greatness. For those of you who were popular in junior high school, the Hulk began as the mild-mannered, though brilliant physicist Bruce Banner, and was transformed into the Hulk after exposure to gamma radiations from a nuclear explosion. From then on, Bruce Banner would morph into the Hulk during times of extreme stress or exigency. While the Hulk’s ability to retain Banner’s intelligence evolved over the series, it’s safe to say he was was never the same again.

So what does growth for greatness look like? It begins with accepting unevenness, and reaches its potential through a conscious nurturing of extremes. But introspection and diligence are not enough. Real growth is scary, hard, periodic, and responsive to your environment. Thegamma ray might seem like an extreme metaphor for catalyzing growth, but if you want to truly achieve greatness, it’s much closer to the reality than the safe, comfortable models we're taught to accept. You need periodic radiation, not lifting a little more weight every day. In the short term, linear development predictably leads to linear results, and in the long term, factoring in drag and the insidious effects of growing comfortable, the result is decline, as Stephen Cohen eloquently described in his conversation with Peter Thiel and Max Levchin. Intelligence is compounding all the time, and correspondingly, so are complacency and missed opportunity.

In practice, it's usually not so straightforward to go looking for gamma rays out of the gate, but there are some obvious pitfalls you can avoid along the way. One of the most important: don’t fall prey to the illusion of growth promoted by the corporate ladder. It’s a crutch as much as a way up (and tech roles/companies are NOT immune - if you see Software Engineer I, Software Engineer II, etc, that’s a ladder). The ladder can be partially explained by convenience, or convention, but ultimately it’s there to assuage your fears – not only of not reaching your potential, but of incubating a potential that doesn’t fit the bounds. While on the ladder, you can only fall so low or climb so high. It's a false frame, not only because hierarchy is such a poor proxy for impact, but especially for lulling you into thinking achievement falls within a standard distribution.

It would be disingenuous not to acknowledge that becoming a mutant is not all upside. Make no mistake, gamma radiation can hurt you. There is always the risk of failure, and win or lose, there will be scar tissue. In that sense the ladder is also a safety net. As an aspiring mutant, you shouldn’t let false bravado obscure this realization – just recognize that in choosing the ladder you’re explicitly shorting your potential and putting protecting your ego ahead of your outcome. As an aspiring Professor X, accept that there will be failures, and that you’ll need to make highly imperfect tradeoffs on false positives vs. false negatives when hiring and developing talent.

Mentorship is likewise critical when directing mutant powers towards the greatest possible good. The X-Men would not have become X-Men without Professor X’s School for Gifted Youngsters. But again, the standard model doesn't apply. To begin with, you need mutants to mentor mutants, and in many cases, to provide the initial dose of radiation. Otherwise, even the best institution of higher learning will predictably devolve into a lemming academy.

Once mutation is in process, one of the greatest aspects of mentorship is, paradoxically, autonomy. This is especially important because extreme growth doesn't happen on schedule, but is subject to periods of intense activity. As a mentor of mutants, you need to be attuned to these periods, and when they come, confer even more autonomy. Above all, fight your instinct to handhold (hard to do when both hands are always clenched in a fist anyway!).

The final part of the equation is to seek out the greatest challenges you can, both in terms of meaning and difficulty. And this is perhaps the greatest beauty of the gamma radiation metaphor. It's not just about unimaginable intensity. It's about an external reality leaving an indelible imprint on your internal reality. There are some gifts that are only fully formed through creative destruction, and it’s these gifts, in turn, that allow you to create new external realities - in other words, to change the world.

This post is about the current insanity in
Silicon Valley, but I don't mean the valuations - at least not the ones
everyone is talking about. Instead, I want to talk about how you value
something much more important than common stock: yourself.

Over the course of thousands of overwhelmingly
positive interactions with top CS students over the past few years, what's
scared me the most is the tendency to think of your future job primarily as a
vehicle for certain types of projects. This is, in fact, one of the worst
possible reasons to take any job.

In many ways this line of thinking isn't so
surprising. Perhaps because the long-theorized tech crash hasn't happened, and
most companies (even relatively innovative ones) think of hiring as filling
slots, our economy continues to promote skills over aptitude and ability. And
even the best schools are much more effective at teaching subjects than
synthesis. As a result, even in an age when software engineers are starting to
be properly valued, there is a real risk of being commoditized - ironically, by
yourself.

Apart from an earnest desire to cultivate
"valuable" skills, however, is something I'll call techno-hedonism.
Besides just thinking of your job in terms of projects, this means evaluating
projects by how pleasurable they are to you versus how much good you're
creating in the world. As a result, topics that could be invaluable as part of
a greater whole - especially things like machine learning - become playthings.
And this is how young people who honestly thought they were going to change the
world end up being paid too much to quit to serve ads more effectively. In the degenerate case your employer becomes
something to be agnostic about, merely a vehicle to work on a specific project
of hedonistic desire.

Rather than deciding based principally on the
project, I would suggest there are two questions that should inform everything
else: Do you believe in the institution? And do you believe in yourself?

Evaluating the institution involves many more
questions, but I'd argue these few are most important: Is there a real
opportunity to make a positive impact? If so, is the team equal to the challenge,
or (more likely) on the path to getting there? Is there a possibility of
surviving as a standalone entity - this is almost impossible to know ex ante,
but if the stated goal is to get acquired that should tell you something. Do
they have a real mission and culture, or just hedonism and homogeneity? Do they
invest in an individual's growth, or just increased productivity?

By believing in yourself I don't mean projecting
an arbitrary level of confidence - it requires a willingness to critically assess your strengths and
weaknesses and reconcile them with an emerging and constantly evolving
sense of purpose. This cannot happen overnight. If you're betting on your
ability to do something important, you'll learn - piece
by piece - to intuitively subordinate the process to the goal, and
separate the act of discovery from the procedural. By contrast, if you're
betting on your ability to stay fulfilled by repeatedly doing a series of
tasks, however pleasurable, you're actually shorting yourself.

It's not so difficult to see the surface
characteristics of an institution for what they are - when you become enamored
of a slick office space, at least you know you're being shallow. Becoming
enamored of projects, on the other hand, feels like investing in your
most important assets when in fact you may be stunting them.

I want to emphasize that this is not happy talk.
It is unbelievably hard work. Having it all figured out now is the unrealistic
part - and if you actually do succeed in your design, that's when the reality
often proves to be bleakest.

Engineering is fundamentally generative. Specific
implementations may be highly deterministic, but the defining character of the
work is possibility. It's understandable to want to cling to certainties,
especially after hearing what a dark and chaotic world it is for most of your
conscious life. I say: embrace conscious ambiguity. The alternative
is a predetermined future - one that truly belongs to the robots. You are not a lottery ticket - but
neither are you an algorithm.

“Do important things” is often invoked as a rallying cry in
these pages, but this time I want to talk about something more important than innovation, invention, entrepreneurship, and all the rest. I want to talk about
dharma. More specifically, I want to talk about your dharma.

Classically speaking, dharma represents both cosmic law and
order – our universal duty - as well as reality itself. Upholding your dharma, then,
refers to both your ultimate responsibility, and upholding the truth. It is no accident that I say your dharma. The truth, while in one
sense absolute, is also deeply personal, and rooted in the enduring power of
the individual.

With commitment to the truth as the first principle, your
code of conduct is simple: When you see something that's broken or bad, you have to say something about it or fix it yourself. Just as
importantly, when you hear something, listen. It’s not just about the success
of the organization, but also a moral imperative not to let anyone you care
about fly off a cliff.

In practice, this is extremely painful. Honest,
unadulterated feedback is as emotionally alien as it is intellectually obvious,
whether giving or receiving. Confronting the truth together is a social
endeavor, yet it flies in the face of all social convention and pleasantries. Unlike
you or me, the truth doesn’t have feelings – but that is precisely why it’s the truth.

Of course, it’s easier to face hard truths when we talk
about collective failures. These are important to address, and can be
invaluable object lessons for the organization writ large. Individual failures,
however, are the ones you, and only you, can control. Accordingly, the most
painful and most vital incarnation of the truth is individual feedback – all in
the service of discovering and fulfilling your dharma.

This matters on multiple levels. In practical terms, nothing
happens unless you make it happen. Day to day, the bias towards action is one
of the most valuable things you can institute.
Without your concerted action, things like planning, analysis, strategy,
et cetera are just distractions from an empty center.

However, dharma is also about the unlocking the essence of the
individual. Facing your dharma means stripping away the pretense, delusion, and
distractions to reveal who you are
and what you are meant to be doing. You
uphold your dharma in the service of both the individual and the collective. For
the whole to be greater than the sum of its parts, the parts cannot seek
anonymity and cover in the whole.

Likewise,true
feedback comes from a foundation of investment in the individual. The
underlying intentions need to include the opportunity to grow from mistakes and
the willingness to help someone get there. We all like to talk about investing
in people, but it’s important to internalize that hiring isn’t the end of the
road. The hard part starts after - especially for the most innately talented
individuals. If you don’t give them feedback, you’re just as guilty of coasting on
their talent as they are, and you will inevitably reap the
consequences.

As many a wise master has observed, there are countless paths to dharma – indeed, there are as many forms of dharma as there are seekers. Everyone
arrives at the truth in a different way, as evidenced by leaders as diverse as
Ray Dalio, Prof. Carole Robin, and Peter Thiel.

Ray Dalio’s Principles is more than required reading at Bridgewater, and
Bridgewater’s culture of “radical transparency” is almost infamous for the
degree to which honest feedback is emphasized. Dalio’s most basic principles states:

“Truth - more precisely, an accurate understanding of reality- is the
essential foundation for producing good outcomes.”

It seems simple enough, but
the real genius of Principles is how
he mediates between the truth as an absolute and the individual experience:

“Above all else, I want you to think for yourself - to decide 1) what you want,
2) what is true and 3) what to do about it.”

Dalio also caveats that “you
can probably get what you want out of life if you can suspend your ego”, and
the same can be said of feedback. For most of us, this will be the hardest battle.

One of Peter Thiel’s great maxims is
“Listen carefully to smart people with whom you disagree.” Thiel is a renowned
contrarian, but he didn’t hone his worldview in a vacuum. One of his greatest
strengths has been assembling teams with the built-in structural tension needed
to confront bias and complacency head-on and do transformative things. To be
frank, this includes the ability pre-select for thick skin. No one who was at PayPal in the early days
would describe it as a touchy-feely place – but factoring in
the type of talent it attracted, that was part of the genius of the design. Pre-eBay PayPal practiced a form of directness that probably wouldn’t have flown at most
other companies – but look at the record of the PayPal mafia versus any other
group of corporate alumni.

Professor Carole Robin of Stanford’s Graduate School of
Business is best known for her popular “Interpersonal Dynamics” course,
affectionately nicknamed “Touchy Feely”. As Professor Robin describes,
“"It's about learning how to create productive professional
relationships," and feedback is a key ingredient. Robin’s approach
may seem like a high-empathy yin to the low-empathy yang of radical
transparency or the PayPal model, but many of the basics are the same. Robin
advises doing it early, and above all practicing often. She also emphasizes the
need to avoid shaming and to “stay on your side of the net” by not making the critique
personal – in other words, don’t aim for the ego. Finally, listening is crucial – in
Touchy-Feely speak, “It takes two to know one".

Recognizing there are many paths to dharma, where do you
start? The most important thing is to take that first step, practicing feedback
early and often, and making it a non-negotiable component of every consequential
effort. To have any chance of sticking, it has to become the new normal.

One of the great tragedies of working life is the tendency
to treat feedback like taxes: a necessary evil to be addressed annually or
quarterly. Too often, feedback is also synonymous with either punitive or
back-patting exercises. You need to inoculate people against these
associations by starting early, before there’s a crisis. Of course, as new
people arrive, you will be forced to begin the acclimation process from
scratch, because organizations that practice truthful feedback as a way of life
are rare, and individuals for whom it comes naturally are rarer still.

Another complication is that people tend to be lopsided in
their feedback. Those with lower empathy have the easiest time giving feedback.
It’s intuitive, even reflexive, but these people tend to be terrible at giving
feedback in a diplomatic way. This is your opportunity to suspend the
ego, assume it’s not a personal attack, and consider the substance of what is
being said. Eventually, you realize that seemingly low-empathy individuals are
often just carrying out their dharma. Make no mistake, it is a gift.

On the other hand those with high empathy are best suited to
diplomatically give feedback, but struggle to make it appropriately critical
because the very thought of doing so causes pain. An empathetic style can
also be a gift, but only when personal sensitivity is complemented by the
courage to overcome the inertial bias against criticism. Above all, recall that
this is the real world. There is no perfect Goldilocks balance. The key
is to get started with the ingredients you already have.

You should also consider the source – except when you
shouldn’t. Remember Peter Thiel’s smart people who disagree with you. With any luck,
you will have colleagues who possess deep credibility in areas you don’t, and
you should make extra effort to listen to them. On the other hand, sometimes
incisive and true feedback will come from people with no apparent legitimacy. When
your ego cries out “who the hell are you?”, turn the other way and focus on the
substance of the criticism.

What if you’re wrong? This is always a possibility, giving
or receiving, but because you are already thinking critically, it’s not a
meaningful risk. If there is any possibility in your mind that something is
wrong, confront it together. Either you avert disaster, or you discover why it
was in fact right. Both are preferred outcomes.

Feedback is especially hard at any meaningful scale. The
larger you get, the tougher it is to guarantee a high standard of intellectual
honesty, while cracks in the foundation become increasingly subtle and
imperceptible. In many ways, it’s good to maintain a healthy reserve of fear of
what you might become - look no further than our political system to see what
happens when the truth is focus-grouped beyond all recognition.

As with almost any worthy endeavor, the pursuit of your dharma
involves constantly raising the bar. It is never easy to ask people to be more
than they have been, and to address when something has stopped working, or
never did. It is doubly hard because these realizations often come when people
are working their absolute hardest. As painful as it is to admit that someone’s
best isn’t good enough, it doesn’t make it any less true. In fact, it becomes
that much more important.

It’s fine to say failure is not an option in moments of
bravado, but you know inside that abolishing failure – at least the lower-case
kind – is not only unrealistic, but leads to denial and paralysis. It’s
entirely reasonable, on the other hand, to insist that you won’t accept failure
without feedback. Only by confronting the day-to-day truth can you hope to unlock
the greater truth of your highest potential, as an organization and as
individuals. That is good karma.

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tag:shyamsankar.com,2013:Post/6575632014-02-24T04:31:41Z2014-05-24T16:21:24ZOptics and the Suppression of Innovation

One of the more pernicious, and also subtler, difficulties
of governance is something I’ll call the tyranny of optics. Across the
organizational spectrum, you find systems that are designed to appear transparent, fair, and free of conflicts
of interest. Yet all too often, the result is gridlock and bad outcomes for the
honest actors, while actual corruption is only pushed deeper underground. It’s
the ultimate bitter irony: instead of functional compromise, you get
institutionalized disaster.

The legacy government acquisitions system is a perfect
example. The driving force is typically not a desired outcome, but rather a
long list of requirements established to pass the eye test. The unintended
consequences of these requirements, combined with their tendency to stifle
innovation, result in the worst of all possible worlds - for the mission, the
taxpayer, and the many people doing their best to both produce and acquire high-quality
technology.

One of the greatest pitfalls is contracting on a cost-plus
basis. This is largely a function of optics, as well as the inherent difficulty
of placing value on high-tech innovation (and the age-old confusion of cost
with value). The problem is that a fixed profit margin means you can only make money by increasing revenue –
there’s no incentive to increase efficiency, even though efficiency is the
whole basis of Moore’s Law. In essence, you substitute accounting for
accountability, and the effect is that the true value of technology, and the true
potential for innovation, are obscured by the very mechanism meant to ensure
transparency. It’s also worth emphasizing that for the vendor, it’s about simple
math, not corruption. When you can only make money on the top line, a rational
actor has no choice but to conform or find a different business.

Furthermore, the system is designed to evaluate the surface qualifications
of a vendor to perform work at the government’s risk – have they done something
like this before for similar clientele? When building massive hardware
investments such as aircraft, this might seem like a reasonable question
(though the success of SpaceX has chipped away significantly at the conventional
wisdom). When applied to information technology, it’s much more obvious what an
arbitrary standard this is - imagine if Larry Page and Sergey Brin had been
subjected to these considerations when they were raising capital. The consequence
is that the number of “qualified” contenders remains flat over time. This, in
turn, creates in an anti-competitive vicious cycle where the presumed ability to deliver is based on
perceived qualifications, rather than those qualifications being based on the actual ability to deliver.

Of course, technology projects fail all the time – but
because optics are paramount, there’s no willingness for the customer or vendor
to admit failure. Instead, we keep sinking money into the same projects until any resolution seems palatable, or the
original need is forgotten. Paradoxically, the system demands perfection, yet actual
failure is shockingly acceptable – so long as the vendors are “qualified”. Because
these failures are overseen by familiar faces, the vetting committee still
boasts a perfect record. It’s like a dystopian version of Blackstone’s
formulation: better ten credentialed companies should fail than one
startup. Consequently, no one is willing to take the kind of development risks
that could yield transformative discoveries. Failures that amount to sunk costs
are acceptable, while the ones that could really teach us something are unthinkable.

A highly respected veteran of Congress and the Executive
Branch once told me that one of the more underreported challenges of DC was
that killing earmarks only removed much-needed grease from the system,
predictably causing the machinery to grind to a halt. Ironically, earmarks connoted
a certain honesty because everyone knew what was going on -The practice allowed
for plenty of valuable give-and-take - the real problem was that in many cases
the optics were just too shaky.

Since the earmark moratorium, we’ve been treated to an
endless game of budgetary chicken that has certainly led to worse outcomes for
taxpayers than earmarks ever did. Meanwhile, conflicts of interest haven’t gone
anywhere – they’ve just reappeared in the form of more insidious slush funds
and legislative blackmail techniques. Technology acquisitions and Congressional
deal-making might appear to be very different beasts, but in both cases, the
substance of compromise and pragmatism has been replaced by the rigid ideology of
covering your backside at all costs. When optics are the primary concern, you
can’t even have token cooperation, let alone the partnership needed to solve
hard problems.

Bill and Melinda Gates’ recent Wall Street Journal editorial, Three
Myths on the World’s Poor, exposes the tragic result of focusing on optics
above everything else. Only a small percentage of foreign aid is lost to
corruption, but that part always receives vastly disproportionate attention. If
the absence of any perceived impropriety became the design criteria for providing
aid or philanthropy, we’d only hurt the very people who need the most help. As
the authors poignantly ask, “Suppose small-scale corruption amounts to a 2% tax
on the cost of saving a life. We should try to cut that. But if we can't,
should we stop trying to save those lives?”

The tax metaphor also helps to expose the rampant cynicism
that preys on optical controversies. Almost no one would consider a small tax, or
other nominal costs of doing business, a good reason to abandon an
overwhelmingly profitable enterprise. Why should the criteria be impossibly
strict when we stand to gain lives as opposed to dollars? Perhaps better than
anything else, the humanitarian aid challenge reveals the logical conclusion of
elevating optics above everything else: since a perfect solution is impossible,
we’re better off doing nothing.

Every election cycle, someone promises to run the government
like a business. Setting aside whether this is desirable or feasible, the
obvious challenge is that the optics become most restrictive when the
government bears the risk (as businesses generally do). Yet vast opportunities
exist for government to transfer risk from taxpayers to suppliers. Imagine a
marketplace where vendors can only compete if they guarantee an outcome or your
money back. Optics would revert to their proper place: still a factor, but far
from being the first or only consideration.

By ending the charade of demanding perfection, we can stop
wasting time on the fantasy of eliminating risk and instead focus on the real
work of managing it. When you practice the art of the possible, paint will
inevitably splatter – but to a realist, the result is infinitely more
attractive than an ideal that will never be achieved.

Without commenting at all on the policy wisdom of the
Affordable Care Act, it’s clear that the rollout of Healthcare.gov has been
disastrous. This has been chronicled more diligently elsewhere, but can be
summed up by noting that, while Healthcare.gov was plagued with bugs, crashes,
and general confusion, a team of three college students replicated much of
the desired functionality of the site in a few days. Of course, the alternative site, HealthSherpa, does not handle the user
or data scale of healthcare.com or perform the most complex operations of
applying for coverage, but the contrast between a site built for free and the ~$600+
million obligated for healthcare.gov is sobering.

We can draw a few lessons from this affair. The first is that it represents a deep structural
problem of government IT projects. The process used to bid out and build
healthcare.gov was not, contrary to what you might have heard, especially
unique or nefarious. On the contrary, it
represents the norm for large federal IT projects: mandating what should be
straightforward products to be built from scratch in many ponderous phases,
replete with massive sets of requirements and a commensurately high number of
billable hours.

The major difference is that this time, the users are the
American people. The frustration of grappling with subpar technology is the
same experienced daily by some of the most vital people in our public service
ranks. Soldiers, intelligence analysts,
law enforcement officers, and veterans care workers, to name just a few, are
routinely forced to implement tools that are barely functional, told to simply “make
it work”. This is by no means meant to
minimize the headaches associated with healthcare.gov – on the contrary, it
points to the need for real, systemic change.

There are two fundamental flaws at work in the legacy
government IT acquisitions model. The first is that the same procedures used to
acquire tanks and aircraft carriers are used to build software. Yet software
development is by nature a creative, generative, iterative process, not a
static set of requirements that won’t change significantly over the lifecycle
of the product. And while good software
is never truly finished, the essential building blocks can often be delivered
right away - the key is that you’re creating a basis for iteration and creative
enhancement, not obediently following the same blueprint for years at a time.

The second, and subtler, flaw is the failure to recognize
that America in general, and Silicon Valley in particular, are unique in the ability to build
software. Many remarkable advantages
of American life have contributed, in turn, to our dominance in software
development. Pondering an increasingly data-driven future, our abundance of
software talent has to be considered one of America’s most strategic resources,
and leveraged and fortified accordingly. Sadly, in the current IT acquisition
landscape, armies of contactors are paid by the hour to produce a crude
facsimile of what our best software artists could create for a tiny fraction of
the cost - but ignoring such a precious asset would be a mistake at any price.

One great irony of the healthcare.gov fiasco is that a major
rationale for the Affordable Care Act was the idea that Americans can do better
than the legacy healthcare system – only to see what should have been a
slam-dunk website rollout crippled from the beginning by the IT acquisitions
machine, another legacy system. Regardless of one’s views about the law itself,
though, one saving grace is made clear: if we want to do better, doing what
we’re already the very best at seems like a good place to start.

One of the most fundamental human desires
to believe that something is either A or B, and many complex endeavors are
compromised from the beginning by treating the A/B split as a first principle.
Binary logic may explain well-understood processes, but eventually the old
rules cease to apply, as with the failure of classical physics to explain
phenomena at atomic and subatomic scales. To understand quantum theory,
you have to accept the wave-particle duality, and even then, it turns out that
no one really knows why light exhibits both wave and particle
properties. We can observe, even predict, but not quite explain.

Startups are subject to similarly misunderstood
dualities. Simple minds want to know if winning depends more on doing A
or B: Should we move fast, or ship
quality? Build footprint or monetize? Optimize
on breadth or depth? The winner,
however, realizes that you have to figure out a way to do both. How this
is accomplished is highly contextualized in practice, but it begins with the
realization that you cannot have one without the other and hope to succeed. If it were as simple as doing only one thing
well, the success rate of venture capital would be much greater than 10%. And
when you do succeed, as in quantum mechanics, recognizing that things work a
certain way is more important than knowing why (for the purposes at hand, at
least).

A venture also displays both
continuous and discrete elements. From a wide angle, the growth curve or
product lifecycle may resemble a wave function, but it’s also extremely
iterative, and is most efficient when individual iterations occur at consistent
intervals. Likewise, one characteristic is often expressed through the
other, much as particle emissions are dependent on wave functions. The focus
and abstraction needed to go broader also allows you to go deeper effectively. Similarly, in the course of developing a
vertical solution, you often end up sharpening
your intuition about how slice the problem horizontally.

When striving to achieve both A
and B, you often need to consciously set up opposing forces to achieve your
goals. For example, you need hackers who are relentlessly focused on
solving the customer’s problems, even if they’re comparatively poor at productization
and long-term code stability, and you need artists who are relentlessly focused
on productization and pristine architecture even if their sense of customer
urgency leaves a lot to be desired. How you make them work together
productively is an art - there is always some violence, but it starts by
recognizing you need both, and accepting that their interactions only need to
be productive, not harmonious. The results of this type of particle collision
are very difficult to know ex ante,
so the safest bet is to find the best exemplars you can of each type – people
you would want to work with individually.

The need to harness opposing
forces sometimes extends beyond types of goal orientation to personality types
(though these often go hand in hand). Again, it’s up for debate why this
is the case, but the anecdotal evidence is extensive. The classic example
from quantum physics is Richard Feynman and Murray Gell-Mann’s collaboration on
the theory of beta decay. Feynman was famously mischievous and
irrepressible, while Gell-Mann was almost painfully serious and
methodical. While they frequently found each other exasperating, their
tension was tempered by strong mutual respect – an obvious but sometimes
overlooked component in organizational design.

Conventional high-tech wisdom
posits that among the qualities of “better”, “faster”, and “cheaper” you can
only pick two. With the right team, you can do extraordinary and counterintuitive
things. You can be better, faster, and cheaper – you just can’t be better,
faster, cheaper, and also comfortable, which is the true contradiction. At the
risk of resorting to truisms, doing hard things is hard - comfort is simply not
part of the equation. As Feynman himself once quipped, “You don’t like it, go
somewhere else!”

Ray Dalio’s Principles is required reading at Bridgewater,
and contains plenty of wisdom that resonates well beyond its original context. Far
down on the list, at #139, we find:

... 139) “Taste the soup.” A good restaurateur constantly
tastes the food that is coming out of his kitchen and judges it against his
vision of what is excellent. A good manager needs to do the same.

Soup tasting is hard and requires you to pierce comfortable levels
of abstraction. Often where there are
bad outcomes, there is a gross lack of soup tasting, both because of inertial
unwillingness to take a bite and because of ineffective gustation.

Amazon’s Jeff Bezos is the archetypal soup taster (among many
other outstanding talents). Bezos is
renowned for the depth of his knowledge and the clarity of his insights
(especially when making snap judgments), but equally important is his ability to
get to the crux of seemingly complex matters in five questions or less. It’s easy to forget how many complex
decisions Amazons has faced over the years, and the fact that their success is
often taken for granted is largely a tribute to Bezos’ ability to ask the right
questions so incisively and consistently.

The importance of soup tasting seems intuitive enough, but how you
develop the ability to taste soup well is one of the more underrated challenges
of leadership for a number of reasons.
To begin with, there is never just one kind of soup. The metaphor applies equally well to the
commercial success of your business and the view from inside. At the same time, not all soup is equally
important, and even the most astute taster’s capacity is limited, so you need a
focal point. As Bezos has often described, “We start with the customer and we
work backwards.”

More fundamentally, soup tasting is largely about overcoming bias,
which is generally a very difficult process.
It needs to be about fearless inquiry, not seeking reassurances. Anyone
who has done any actual cooking has probably had the experience of asking
someone else if a dish tastes funny, while silently convincing himself that the
answer is no. Of course, if it does taste funny, being polite does the aspiring
chef no favors. For soup tasting to have
any value as an exercise, you can’t be afraid of what you might discover.

Soup tasting is as much art as science, and as such it is hard to turn
it into a predictable framework. Still,
some basic principles apply:

It
all starts with probing.
Any time you are presented with an assertion, whether it’s a project
plan, forecast, or report, review it tenaciously. If something isn't clear to you, probe down. If
something strikes you as particularly important, probe down deeper. If there
are implicit assumptions, challenge them. Think of the annoying little kid who responds
to everything by simply asking “why?” It seems repetitive, but if you proceed
from the right starting questions you will quickly get to the heart of the
matter.

Get
closer to the problem. Something about the soup seems off. Now you need to taste it some more. The first step in getting close to the
problem is simply a more thorough probing.
If that doesn’t do the trick, you need to go down two or three levels,
either by honing in on the most important things in your area of credibility, or
by asking someone who is credible. By
the way, assessing who has credibility in what areas, beyond just being aware
of their reputations, is its own important form of soup tasting.

Measure.
Soup-making, both literal and figurative, requires experimentation, and
it’s one of the hallmarks of the Amazon approach. Bezos places a premium on experiments that
are measurable and produce hard data. As
he explained in Fast
Company, “The
great thing about fact-based decisions is that they overrule the hierarchy. The
most junior person in the company can win an argument with the most senior
person with a fact-based decision.” At
the same time, as Bezos will quickly tell you, “there’s this whole other set of
decisions that you can’t ultimately boil down to a math problem” – hence you
need to master the art as well as the science.

It’s also well worth considering what soup
tasting is not:

It’s not micromanagement. This
means telling people how to do something without tasting the soup for yourself,
or telling them how to do something in an area where you lack credibility.

It’s not distrust. Distrust is
not a productive default position, but neither is blind trust. Real trust is
developed by consistent soup tasting – as the old saying goes, “trust, but
verify”. Knowing which issues to escalate as priorities, and how to
escalate them as a team, is also an art form, honed through soup tasting
interactions.

It’s not indefinite, nor is it an end
in itself. You need to find the middle
ground between an excessively laissez-faire approach and never-ending
inspection.

The more soup you start to taste, the
more you'll want to taste, but as with anything, you can overdo it – just as
you can proofread too long, and you’re bound to miss something obvious. It
is critical to cultivate credible soup tasters throughout the organization, but
the transition from soup taster to meta-soup taster is a tough one. It only
works if your trust has been validated, and requires a great deal of
intellectual honesty to avoid indulging in wishful thinking, feel-good exercises,
or just shedding responsibility.

In the end, soup tasting is how you
know what is true – “overcoming bias” and “intellectual honesty” are really
just fancier ways of expressing this. And the truth matters more than
anything else. In his introduction to Principles, Dalio states,

“I also believe
that those principles that are most valuable to each of us come from our own
encounters with reality and our reflections on these encounters – not from
being taught and simply accepting someone else’s principles…. So, when digesting each principle,
please…

…ask yourself: “Is it true?”

All soup tasting, ultimately, is a
variation on this one simple yet profound question.

I think a lot about what specific competencies are needed
when starting something, but even more fundamentally, how does someone approach
work (and life)? My experience is that there are goal-oriented people and there
are process-oriented people. Finding
goal-oriented people is one of the most crucial determinants of startup success
- no amount of expertise can substitute for goal orientation.

There is implicit bias in both orientations, but not all
biases are created equal. Goal
orientation subordinates process to outcomes.
As a result, there is sometimes a tendency to ignore or undervalue the
importance of frameworks, checklists, and details, though in my experience
truly goal-oriented people are quite intuitive at abstracting useful and
repeatable approaches from their experiences. Planning and process are also not
the same thing – done right, planning is simply the division of larger goals
into smaller ones. Even so, goal
orientation is a vastly preferable bias.
You can learn organization (and the most effective people are constantly
re-learning it), but motivation is much harder.
By the same token, consultants can help to improve your processes, but
they can’t define your goals for you.

Process orientation, on the other hand, actually subverts your
goals, under the subtle guise of helping you achieve them. Uncritical acceptance of process creates an
alibi for failure. When things go wrong,
a process-oriented person thinks “I did all I could while following the process
to the letter, so maybe it just wasn’t meant to be.” Without a healthy
institutional skepticism, process easily becomes a goal in itself. To be fair,
processes and goals can both be destructive if they are not subject to revision,
but process is fundamentally tied to predictability and routine, whereas goals
require constant thought and re-examination to remain effective.

The most inventive organizations are more concerned with
limiting process than perfecting it. Apple’s revitalization began when they
started to re-imagine a hardware problem (personal devices) as a software
problem. If process had been the dominant consideration, Apple would have kept
refining their old product lines until they faded into irrelevance. By the same token, many enormous failures
affecting society writ large can be attributed in part to relying on process
while ignoring the substance (Enron, the subprime collapse, countless failed
technology acquisitions).

Everyone claims to be goal-oriented (it’s probably one of
the top resume clichés), but the norm is that people want to be told what to
do. Freedom is scary, partly because it
is new and unfamiliar, but mostly because the onus will be on you to succeed once
the security blanket of process is taken away.
Truly meritocratic and goal-oriented organizations are also quite rare,
so it’s easy to mistake boredom and frustration with bureaucracy for real
self-determination. During both Internet
bubbles, countless career big-company employees decided they wanted to “join a
startup”, without really asking why or realizing that they were trying to be
different in the exact same way as everyone else (the word “join” isn’t an
accident either). Ironically, when
asked by hiring managers what they
would bring to the table, these people would typically deliver lengthy homages
to their current company’s processes.

One of the most interesting things about goal and process
orientation is what part is constitutional and cultural. Some people are natural insurgents, who will
orient and achieve the goal so intuitively that they may not even appear
disruptive to the casual observer.
Others have been raised in cultures that value conformity and
process. Just as many genes are only
expressed when the right stressors are present, a naturally goal-oriented
person may not emerge until landing in the right environment. The converse is
much less common, however – process-oriented people tend to be exposed fairly
quickly in truly goal-oriented environments where there is little concept of
playing along.

The conflict between goal and process orientation is
exceptionally relevant to planning one’s career. We’ve all seen picture-perfect, cookie-cutter
resumes that are obviously a result of process orientation,. What’s more interesting is when people try to
design rules and processes to reverse-engineer a major career shift. There are plenty of “experts” who will tell
you to get experience in the private sector before doing a stint in government
(or vice versa), or that you should learn “fundamentals” at a Fortune 500 company before joining an
early-stage startup. With all due
respect, these people completely miss the point of having goals. It should be more obvious with really
unorthodox career arcs, but even so, many people are apt to read about Steve
Jobs and think “Ok, so I should drop out of college, but take a calligraphy
class, and get fired from my own company before making a triumphant comeback.”

Of course, there are plenty of perfectly good environments
for process-oriented people. The problem
is when they land in the wrong place and both the person and team suffer. It really comes down to honestly understanding your strengths and
weaknesses, as an individual and as an organization.

]]>
tag:shyamsankar.com,2013:Post/3849472013-04-12T03:53:53Z2013-10-08T16:44:49ZOn the Joy of Renting

Ownership is the essence of the American Dream – or is it? The
mortgage crisis certainly led many people to rethink the virtues of owning a
home, but even in less dramatic markets, it’s a fair question. There are many assumptions to be challenged
and hidden costs to be considered.
Warren Buffett continues
to bet heavily on housing, while Yale economist Robert Shiller contends that
housing is an investment fad, with no net appreciation in the US market
over 100 years. Of course, as author of
the Shiller post points out, most of us are living in our homes, and the benefit
is partly intangible. But how much does
the intangible actually depend on ownership as opposed to just being there?

Rental has always been a popular alternative for major,
long-term-use assets with high maintenance costs. Traditionally
this has meant homes and cars, but they are just the beginning. The convergence of low-friction technology,
on-demand efficiencies, expanding tastes, and shrinking wallets has led to the
explosion of the sharing economy, as
reported by The Economist. There are
countless examples, each with its own intricacies: Rent The Runway, Amazon Web
Services/EC2, ZipCar, Uber, even BlackJet. It’s about deciding not to own something
you really don’t need to own yourself (and achieving better financial health as
a result).). Increasingly, we have the
option to spread out the long-term maintenance cost, which actually exceeds the
acquisition cost for more assets than people tend to realize, while maintaining
high availability.

The sharing economy ranges from necessities such as housing
and transportation to luxuries such as designer
dresses and private jets but necessities quickly become luxuries when
acquired carelessly. This is especially
pertinent for government, but it’s not always obvious which costs justify
themselves. Traditionally, the Forest
Service, Coast Guard, police, et cetera all maintained their own helicopters,
for example. Even if they were grounded
90% of the time, no one wanted to give up ownership if they had a choice. Now that states are going broke, sharing is a
much more palatable option, but it’s not just about cutting costs – you have to
re-examine the incentives. In government, one of the major drivers of ownership is funding.
It’s easier to get large capital funds for new assets because they are
assumed to be investments— and investment has a return. It’s much harder
to get operational funding because that is a cost - and costs are bad, right?
(how many times have you heard the renting is throwing money away?) But
what if that helicopter fleet is just a really bad investment? It becomes a lot
easier to make that case if you can get a
helicopter on short notice, probably based on a retainer and/or hourly use fee
(similar to ZipCar).

Separating the emotional appeal of ownership (as difficult
as that may be), my thesis is that it is generally a bad idea to own an asset
unless you have a specific and special competency to own it. This is the same for everything: housing,
cars, servers - and especially software.

Cars are a tricky
case, famously depreciating (up to 10%) the minute you drive them off the lot (a
phrase so commonplace you probably finished it in your head). Many of us don’t
know how to truly maintain our cars beyond the basics. For occasional drivers, there is the lesser
option, such as ZipCar, but US infrastructure is still designed around
individual drivers, and giving up your car can be very difficult if you don’t
live in a city. However, something like Sebastian
Thrun's self-driving car work could someday open
up a whole new world of on-demand transportation that is more efficient and
safer than anything we have now. Think
about it: 97% of the time, your car is sitting around, taking up space, idle.

Servers, beyond
the fixed costs, require hardware maintenance, networking, power and
cooling. Many servers require
replacement after just a few years. It’s
much easier and lower overhead to simply rent the capacity you need - unless
you are Google, Amazon, or the like, and have a special competency that
requires you to maintain your own servers.

Software is often
perfectly suited to on-demand delivery for predictable use cases, and
software-as-a-service (SaaS) certainly qualifies as one of the major technology
waves of recent years. More and more,
the prevailing sentiment is “why buy software when you can rent it?”, as
reflected in Salesforce’s now-iconic
logo.

Of course, not all software needs can be satisfied by
SaaS. Then the relevant question is
whether to build or buy, as opposed to rent or own, but the underlying
considerations are similar (if quite a bit more complex). My guiding principle is that you shouldn’t be
building your own software unless you have a particular competency that
requires it, or need to develop such a competency.

In keeping with the theme of recognizing our own biases,
it’s important to separate the emotional resonance of ownership from the
practical reality. With software, the
reality is that code depreciates incredibly fast, not to mention the continuous
iteration and improvement required for software to stay relevant. Ownership
bias is perhaps most frequent (and outsized) in government, where the idea of “owning”
the code base has become hugely and irrationally popular. In the vast majority of cases, “building” and
subsequently owning your own software actually means contracting with private
vendors to develop complex, bespoke systems that cost 10, even 100 times as
much as an off-the-shelf product.

There is an attractive yet perniciously false idea that once
you build the software, it’s yours, free and clear. The appeal is simple - people enjoy the
feeling of ownership, and are naturally wary of being beholden to outside
vendors. But the reality is that you are
paying down accrued technical debt all the time – just as you would maintain a
house or car, except that a house or car isn’t expected to fundamentally change
in a matter of months. Furthermore, a
bespoke project concentrates that debt with one client instead of amortizing it
across all customers the way a productized solution does. In a very cynical way, bespoke developers are
smart to let the government own the source code. Not only does this prevent
other customers from re-using the IP (and saving money on development), but it
also makes the ongoing maintenance costs easier to justify because now, it’s
their baby.

The final point is that if you are going to buy, you need to
make sure that the seller has a specific competency in software. It might seem obvious, but more than any
other product, you want to buy software from a software company. Rolls-Royce
can build world-class cars and jet engines alike, but there isn’t really an
analog in the world of aerospace companies and systems integrators that also attempt
to build software. The product
lifecycle, pace of innovation, maintenance considerations, and above all the
deltas between good and great all make software unique among industries.

If you’ve spent any time in high tech the last few years,
you’ve probably heard the term “big data” more than you care to recall. It’s become a constant refrain, and the
subject of plenty of breathless cheerleading, much like “the cloud”, “social
media”, and countless other trends that preceded it. This is not to say that big data is not
important, but context and meaning are essential. Big data has many roles to play, but it’s not
an end in itself, as Shira Ovide explains so concisely in her recent
Wall Street Journal piece.

“Data for data’s sake” is the first major weakness of the
big data obsession cited by Ovide, and it’s probably the most salient. This a classic case of valuing inputs over
outputs – the idea that if we only collect enough data, good things will
happen. This sort of magical thinking is
somewhat reminiscent of past crazes for purely A.I./algorithmic approaches to
data science, but at least in those cases there was some concept of outputs and
programmatic attempts at sense-making.

Of course, big data also isn’t going anywhere, and many
worthy analytical endeavors demand that we address it. However, it is essential to distinguish
between warehousing, searching and indexing, and actual analysis. Focusing solely on storage and performance
creates a sort of computational uncertainty principle, where the more we know,
the less we understand.

As Ovide also notes, there is also a critical gap in
analytical talent, which big data has done more to expose than mitigate. Computing power can go a long way towards
making big data manageable and facilitating insight – if paired with a sufficient dose of human ingenuity. Simply put, humans
and computers need each other.
"Pattern recognition” is frequently cited as a benefit of a big
data approach, but computers can't learn to spot patterns they've never seen. As a result, the value of the analyst in
defining the correct patterns and heuristics becomes all the more important.

Appropriately enough, the most valuable and elusive elements
lurking within big datasets are often human: fast-moving targets such as
terrorists, cyber criminals, rogue traders, and disease carriers who tend to
slip through the cracks when algorithms are deployed as-is and left
unattended. The old playground retort
that it “takes one to know one” actually applies quite well to these types of
situations.

Human capital is a key part of the equation, but it’s not
enough to acquire the right talent – you need to address the inevitable
organizational challenges that come with retooling for a big data future. Ovide notes that many companies are
installing “Chief Analytics Officers”, and while I want to reserve judgment,
the cynic in me suspects this reflects the bias of large organizations to
centralize power and create new titles as a first line of defense against
unfamiliar problems. A chief analytics
officer could be the catalyst to
instill readiness and analytical rigor throughout the organization, but whether
this reinforces or dilutes the perception that big data is everyone’s concern
is a fair question.

More than anything else, I would analogize the challenges of
big data to the differences between conventional warfare and
counter-insurgency. In conventional
warfare, the targets are distinct and obvious.
In counter-insurgency, the enemy is hiding among the population. Much as you can occupy an entire country
without knowing what’s really going on outside the wire, you can warehouse and
perhaps even index massive data stores without producing actionable
insights. Effective big data approaches,
like effective counterinsurgency, require the right balance of resources, sheer
power, ingenuity, and strong and constant focus on outcomes. In the long run, the willingness to pursue a
population-centric strategy may well prove to be the difference.

]]>
tag:shyamsankar.com,2013:Post/2438402013-03-27T23:46:35Z2013-10-08T16:13:37Z1776 The Ultimate Story of Entrepreneurship

David McCullough’s 1776 is, to my mind, the ultimate
story of entrepreneurship. Starting a
company is challenging enough - now imagine starting a country! Although many orders more complex, America’s
founding has much to teach entrepreneurs of all varieties. And given this heritage, it should also come
as no surprise that the United States remains the best place in the world to
start something new.

One of the most valuable things 1776 imparts is an appreciation for the
incredibly hard fight endured by the Continental army. If your most recent lesson on the American
Revolution came from a high school textbook, you might dimly recall a few
triumphant battles and Valley Forge. 1776 paints a vivid picture of the sheer
misery and constant trials of the war – trials few could have anticipated. The Continental Army’s perseverance is even
more impressive when you realize that the Treaty of Paris wasn’t signed until
1783. For the modern reader, it’s a
nuanced lesson: on one hand, you need to be realistic about the challenge
ahead, but at the same time, you have no way of really knowing.

The parallels between startups
and the Continental army are fascinating.
Some quick observations:

Chaos: Compared
to the British army, the Continental army seemed completely chaotic. There
were no well-defined roles and no visible hierarchy among these ragtag,
shoeless countrymen who had taken up arms. Of course, some of this
chaos was real and some was perceived. The relevant point when
starting anything is not how to eliminate chaos, but rather which elements
of chaos should be tackled in what order. Do you address real
organizational challenges, or just shuffle everyone’s title?
This distinction escaped the British, who underestimated the strength and ability
of the “rebels” simply because they looked like a mess.

Meritocracy. Nathaniel Greene and Henry
Knox are two of the better examples. Greene, a Rhode Island Quaker who
had never been in battle before, became Washington's most trusted general
due to his exceptional competence and dedication. Knox was an obese
25-year-old who rose to the rank of Colonel. He thought up the
mission to secure artillery from Ticonderoga, without which the Continental
army would have had no such capability.

Talent: Despite
Washington’s minor experience in the French and Indian Wars, his principal
strength was not military strategy (in fact, his advisors staved off
disaster more than once by convincing him not to do something). His real superpower was his ability to
quickly determine who was
talented at what.

Food: Food
was critical to the Continental army. Certainly there
were times where they were on the move and hardly ate for days on end. While food was always scarce, the fact
that the Army was actually able to feed people with some consistency was
critical. The modern startup is obviously not directly comparable, but we’ve
seen time and again how providing food pays for itself many times over in terms
of focus, productivity and commitment.

But more than simple observations
and parallels, there are some real takeaways and strategies for anyone who
aspires to start something extraordinary:

Be Ruthless.

I was shocked by how many times
during the course of battle the British would halt their movement to rest or make
porridge or something completely non-essential. There were countless occasions
where the side with the advantage could have ended the war, had they only
pressed on. Their reasons should sound a
cautionary note even now - stop because it is getting dark? Stop because
that was the plan (despite the ground truth)? Worst of all: stop because
we can finish the job more comfortably tomorrow.

After routing the Americans and
forcing them across a bridge, British General Cornwallis decided to rest.
The Americans retreated brilliantly and swiftly into the night. This was
not the Continental Army's first such retreat, so it’s hard to imagine how
Cornwallis did not realize the significant risk they posed. Why didn't he send
out patrols? Most likely, he thought he would win tomorrow regardless, and
preferred not to win under uncomfortable circumstances. After the fact, he
said that he would have kept going, whatever the risks, no matter the orders, if
he had only known he would have caught Washington. The lesson: Be ruthless as a default setting, not just because
victory is seemingly at hand.

Don't Get Overconfident.

Nearly every major mistake by
either side in the 1776 campaign was a result of overconfidence. Minor
victories would lead commanders to discard their hard-won knowledge, resulting
in terrible decisions. The tendency to let encouraging signs override our
better judgment is actually a fundamental human cognitive bias. If you’re interested in learning how to
recognize and defeat all manner of non-rational thinking, make it a point to
read Overcoming Bias.

Don't Waste Time Politicking.

General Charles Lee felt slighted
that the less experienced George Washington was given command of the
Continental army, and constantly sought to undermine him. When Washington
ordered Lee to bring his forces to New Jersey, Lee dawdled, and was captured by
the British while seeking some female companionship in a tavern. Lee was marched to New York in his nightgown,
and soon defected. Much more devastating,
however, was a series of letters to Lee from Washington's close advisor and
friend Joseph Reed, detailing Reed’s disappointment with Washington. Why
couldn’t Reed have an honest, face to face conversation with his brother in
arms to
sort through the issues? In any vital endeavor, there is too much at
stake to have closed communications or privately nurse resentments.

It ain't over 'til it's over.

Time after time, each side
thought a specific battle was going to be decisive. In retrospect, it is
amazing how incredibly wrong they were, and how often. So how do you
respond? There is a fine line between being jaded and being realistic. Starting
something invariably requires commitment in the face of uncertainty. For this reason, I’d argue that it’s better
to be optimistic (even if slightly naïve) than completely cynical, but again,
the key is to be aware of our biases.

According to a recent Wall
Street Journal article, business schools are placing increased emphasis on
the employability of their students prior to admission. I won’t speculate to what extent this is
motivated by the need to protect their job placement statistics in a grim
economy, but it’s worth considering the true consequences of this trend. As the article notes, business schools have
always considered the goals of the applicant – but to what extent are they
curating these goals on the front end?
Even if we assume good intentions, the effect is to reinforce the status
quo, making business school populations even more risk-averse and less
entrepreneurial.

Ironically, this seems to be at least partly motivated by
the banking collapse: “when the financial crisis upended the banking sector and
sure-thing jobs on Wall Street disappeared, schools began formally tying input
(applicants) to output (graduates).” Why
“ironically”? Regardless of how much
blame you want to assign to federal housing and lending policy as opposed to
private sector recklessness, the financial crisis wasn’t brought on by entrepreneurial,
non-linear thinking. Legions of conventionally smart people who had done
everything right, rigorously following twenty year plans including name-brand
firms and business schools, managed to get the biggest bets horribly
wrong. This is not meant to be flippant
– current market conditions and job statistics are stubborn things that must be
acknowledged. However, if the lesson of
the financial crisis is that we should double down on conventional wisdom,
regardless of whether anything of value is created, then we’ve indeed learned
nothing from the past five years.

As someone who frequently uses the frame of inputs vs.
outputs, I took immediate notice of the wording above. It would be encouraging to see an extremely
input-focused sector more concerned with outputs, but I suspect they have
confused the two in this case, merely trading one set of inputs for another (the
addition of an MBA). You can also think
of this as commoditizing human capital, and this calls the entire purpose of an
MBA into question. Is business school
meant to, help develop leaders, or serve as a finishing process on a
prestigious kind of assembly line?

The article goes on to state that “making employability too
weighty a factor in admissions can backfire. “ According to Graham Richmond, a
former admissions officer at University of Pennsylvania's Wharton School, “Looking
at applicants through a narrow vocational lens may deter schools from accepting
riskier candidates, such as entrepreneurs or career-switchers, in favor of more
sure things, such as aspiring management consultants.” The fact that aspiring management consultants
are considered “sure things” is evidence of how much MBA culture values process
over invention. Candidates and schools
understandably want assurances, especially in the wake of 2008. The world is a chaotic place, even more so
since the financial crisis (though I contend that it has always been so, and
that the banking industry simply managed to insulate itself unusually well for
as long as it could). Obviously, you
have to adapt to the current reality. Yet
I can’t help but wonder if by focusing on doing obvious, “safe” things, to the
exclusion of risk-taking and creativity, the MBA community isn’t just
constructing an elaborate playpen in which nothing new ever happens.

One of the most startling yet largely under-reported facets of the European financial crisis is the rate of youth unemployment, especially in Southern Europe. If you are a young person in Greece (58%), Spain (56%), Portugal (39%), Italy (37%), or France (27%) you are likely looking elsewhere already. There are certainly nearby places with a shortage of qualified workers (such as Germany), and when any job is scarce, it may seem a strange time to be seeking your ideal job.

Yet, for those of you who studied engineering (especially computer science) that is exactly what I am suggesting. Palantir is hiring aggressively in Palo Alto, New York, Washington, Los Angeles, London, Australia, New Zealand, Singapore, and beyond. If you are not only technical, but also passionate about using technology to address problems that matter most in the word, Palantir (and I personally) would love to hear from you. Why Palantir?

Meritocracy: Silicon Valley has the highest concentration of great computer scientists of anywhere in the world. If you are a gifted young computer scientist, you belong with a Silicon Valley company if not in the Valley itself. Of all the great things about Silicon Valley, meritocracy may be the greatest differentiator. There are no long apprenticeship or trainee programs at Palantir (though we are always learning). Everyone is equipped to begin working on real problems within weeks. Good ideas don’t have to pass through a massive hierarchy - the best idea wins, regardless of whose idea it is.

I spend a lot of time thinking about delivery models for technology, especially in an age of shrinking budgets and growing complexity. So I was struck to read that Avanade, a joint custom software venture between Accenture and Microsoft, had been sued by a customer for major cost overruns. The key part:

The lawsuit said a software project estimated to cost $17 million and take 11 months instead mushroomed to $37 million over three years, and ScanSource said it still doesn’t have a Dynamics software up and running. Accenture has estimated it will cost $29 million more to complete the ERP project, according to ScanSource’s lawsuit.

What can be learned from this? There are quite a few things. The cynics among us might point out that an overrun of $20 million and 2+ years is considered a bargain in some areas of government. That is of course an outrage, but the important takeaway goes beyond the numbers, to the fundamental nature of the delivery model. Let’s assume for this conversation that actors all good faith and very competent here. I think that despite that, the model leads to these sorts of outcomes.

Not surprisingly, Avanade turns out to be in the business of renting labor. Services is the exact wrong model – a catastrophically incorrect model, the more you think about it. These sorts of incidents are really a lagging indicator of the weakness in the model, but it’s taking a whole lot of innocent (and some not-so-innocent) bystanders with it. More on them in a few.

There are many shortcomings to services model, but most fundamentally it’s the wrong incentive structure. When you’re renting labor and other nebulous inputs, it’s almost a truism to point out that the longer it takes, the more the company prospers, and the bigger the project, the more room for abuse. A contractor doing a bathroom remodel might employ a similar cost structure, but could never get away with overruns on a tenth the scale of those alleged in the Avanade lawsuit. Of course, even if you have reliable cost safeguards in place, custom software development is inefficient, as I’ve often railed about in these pages. It takes an army of consultants to deliver, and another army of consultants to maintain.

It’s not all the services company’s fault, though – not even primarily. In a sense everyone is complicit, from the services company, to the customer who doesn’t demand something better or structure payment to be a premium but based on success, to the tech giants who aren’t working to productize services. Of course, if product companies dared to do so, the services companies of the world would throw a fit, and professional courtesy runs deeper than you might think in a theoretically competitive marketplace.

When the world changes, you don’t always get a say in the matter, and evolution has a funny way of sneaking up on those who get too comfortable. The first indications may just be bubbling to the surface, but two things are clear: services companies are under tremendous pressure, and product companies need to productize services.

The first point makes sense from a valuation standpoint. Mature tech companies such as Oracle and Microsoft have market caps of ~5-6x annual revenue, while the multiple is often less than 2x for services firms, even the upper tier. Yet it’s still not obvious to all that services companies are living in the past (partly because many services companies are so good at convincing people they’re really technology companies). Mostly, though, it’s because services companies still generate a lot of money. It’s a dying model that’s still making people rich, so it’s easy to ignore the warning signs even if you see them. And for an exponential trend, by the time you are 1% there, it is almost done. You could almost analogize it to the SUV craze: consumers couldn’t get enough gas-guzzling SUVs, and American auto makers happily served them up for several years. Suddenly (but not all that surprisingly), $3-4/gallon gasoline was a fact of life and those same automakers were all teetering on bankruptcy for giving the customers exactly what they wanted.

In terms of multiplying complexity and data problems, we’re entering an era of $10/gallon gas. Even if you’re in the product business, if you’re not increasing your productivity per person, you are dying – in some cases more quickly and dramatically than the services dinosaurs. And for this reason, product companies can’t just deliver products any more – they need to productize services on a continuous basis. In short, they need to deliver outcomes. Mere capabilities only work against well understood problems. They won’t be sufficient for the types of challenges that grow appreciably bigger in the time it takes to read this blog post.

If that sounds smug, it needs to be acknowledged that building a business based on outcome delivery, as opposed to a static product, is still extraordinarily hard. Not only are the prevailing incentive and cost structures far behind, but technically speaking it’s a very rugged frontier. This is perhaps best illustrated by software, where performance at scale, processing, security, stability, and interoperability are often much bigger challenges than achieving the desired functionality. On the other hand, though, successful technology has always productized services of some kind, dating back as far as the cotton gin or even the wheel. The entropy of the present and future data landscape adds an enormous degree of difficulty, but along with Moore’s Law, the single biggest lever of the knowledge economy is the ability to repackage experience and lessons learned into a better, more responsive product. It may take years or even decades, and it’s entirely possible that the first mover will end up being a sacrificial lamb. Sooner or later, though, the company that gets productization right will eat the legacy companies’ lunch.

A defining difference between Silicon Valley and the Old World is that Silicon Valley is intensely focused on outputs as opposed to inputs. While the shift to an outcome-based economy remains a work in progress, the high-tech world tends to focus on tangible results, not ingredients. It’s not just about a different way of thinking about business – it’s a matter of different societies and what they value.

One of the original inputs is ancestry. No one in Silicon Valley will ask you who your parents were or what they did, whereas people absolutely will in the Old World and East Coast. At some point in American history, having ancestors who came over on the Mayflower became an indicator of New England aristocracy – funny when you consider that the Pilgrims themselves were people of no social standing, building something from scratch.

Input bias is easy to observe in classically process-oriented companies (and societies). Fixation on research and development is a prime example: the value of the final product is judged by the input (“it cost us $500million to develop this”) more so than the results. Spending in general is frequently touted as an absolute good or evil ipso facto, but it’s actually one of the least relevant data points on its own. When we talk about confusing cost with value, we’re really talking about confusing inputs with outputs.

Wall Street is extremely focused on inputs, even though their efforts are ostensibly measured by outputs, and fairly straightforward ones at that. On Wall Street, input doesn’t just refer to assets under management – it’s about name-brand firm experience, having an MBA from the right school, who designed your suit, even your business cards. Ironically, Goldman Sachs, the biggest name on Wall Street, transformed itself from a struggling, undistinguished firm to the world’s top investment bank under the leadership of Sidney Weinberg, a junior high school dropout. Weinberg was originally hired at Goldman as a janitor’s assistant making three dollars a week – an anonymous and menial job, certainly, but a job at the firm judged solely on output.

Where you went to school is an obvious input, but outputs matter for the endurance and success of the school itself, especially young schools. How did Stanford, founded in 1891, achieve equal footing with the Ivies? Money certainly helped, but intermingling with Silicon Valley and entrepreneurial culture played a much greater role than simply having wealthy donors. From legendary engineering dean Frederick Terman, who mentored (and invested in) Hewlett and Packard, to the founding of Yahoo! and Google by Stanford grad students, to Peter Thiel’s recent course on entrepreneurship, Stanford and Silicon Valley have enjoyed a unique symbiosis. In terms of clear outputs, a recent study found that companies founded by Stanford alumni create $2.7 trillion in annual revenue. Beyond pure productivity, Stanford arguably introduced the concept of great entrepreneurs as a tangible output of a university, mentioned in the same sentence as Nobel laureates and world leaders. The willingness of many of these great entrepreneurs to reinvest not only their money but also their wisdom and mentorship into the university is one of the great virtuous cycles in education.

Perhaps the ultimate input is age, and when a society values something simply for being old, it speaks volumes – especially when that something is itself. The output that matters is enduring impact and relevance. For the Old World, the danger is that reverence for the merely old is so deeply ingrained that by the time a society realizes it’s stagnating, it is exponentially harder to reverse the tide – witness the number of once-great empires of Europe struggling to stay afloat. The United States is an obvious counterpoint (not that we can take that for granted), and I’ve often reflected that Silicon Valley values are really American values writ large, but there are new revolutions happening all the time, even in very old societies. China and India were home to ancient and storied cultures, though neither was a world power as recently as the mid-20th century. Today, in a post-imperial, post-Soviet world, they are major players, buoyed by high-tech explosions that would have been unimaginable fifty years ago. Yet I would argue that such transformation only became possible when China and India collectively decided that only outputs, not the systems that produce them, are truly sacred.

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tag:shyamsankar.com,2013:Post/744802012-11-16T02:32:00Z2013-10-08T15:37:34ZFocus on the First Derivative

In a fast growing company, everyone has less experience than they need for their roles, by definition. This will continue to be true as the company scales, one's role changing in a fundamental way every 3-6 months, especially when it continues to defy expectations for months and years. Ultimately, that’s all irrelevant. In Silicon Valley, we like to talk about visionary leaders making momentous decisions amid great uncertainty, but what really matters is the first derivative of understanding: how are you and your team learning from the experience as it unfolds? There are many considerations nested in this question – here are some of the most important:

How quickly are you learning? When you are operating within a tornado, speed counts for a great deal. It’s often been said that even the right decision is wrong when taken too late, and this begins with learning. If the second and third order effects of your original challenge are already on an irreversible course by the time you’ve grasped the nature of the challenge, it’s no longer the same challenge.

Are people taking the same things away from failures? In an ideal world, everyone would not only draw the same conclusions from the experience, but they would also be the correct ones. More often, the process is a lot messier, but that’s just reality – you learn together through give and take, not some mystical collective unconscious. The key is that you are unified about your next move.

Are you making meaningful abstractions, or just reacting to your immediate circumstances? Even when execution is everything, there is such a thing as being too tactical, and morale plummets when people can’t make abstractions (or they aren’t taken seriously). It’s a delicate line, because your abstractions have to be actionable and part of a continuous cycle of learning and responding.

When dissent occurs, is it productive? Just because you eventually arrive at the same takeaways doesn’t mean there is no room for disagreement. The question is whether it’s healthy and constructive, or pointed and personal. The “team of rivals” concept has gained many adherents in recent years, but it’s important to remember that it’s above all a team. Ideally, iron sharpens iron.

Three Two strikes and you’re out. In certain areas, such as distribution, you don’t get many chances to course-correct when one approach fails, so extracting the right lessons from the first failure is paramount. This is not to say that you should impose needless anxiety on these kinds of decisions, but be aware of what the stakes are.

Can you reform your model? Models can be extremely useful and necessary to consolidate your understanding of a complex world and plan accordingly. However, they can also be an especially insidious kind of blindfold. Adjusting your model, or abandoning it when necessary, can be incredibly difficult, because it requires you to first recognize and confront your inherent biases, and resist the tendency to rationalize away the model’s shortcomings.

In a hyper-growth environment, you will never have enough information, experience, or foresight. The first derivative will be the only thing that matters. We became the ultimate learning animals through many unforgiving eons of natural selection. This new evolutionary challenge of warp-speed learning and adaptation may feel significantly more abstract, but once again, it all comes down to survival.

If you have more than a passing interest in the future – be it yours, your venture’s, or humanity’s writ large - Peter Thiel’s CS183 lecture #13, “You Are Not A Lottery Ticket” is a feast for thought. Thiel interrogates the underpinnings and consequences of determinate and indeterminate worldviews in numerous contexts, including as they apply to startups.

For the aspiring tech entrepreneur, one of the most useful frameworks Thiel invokes is that of calculus (determinate) vs. statistics (indeterminate). In calculus, you make precise determinations, often concerning discrete futures. You can figure out exactly how long it will take to drain even the most irregularly shaped swimming pool. And this enables you to do things of vital importance. As Thiel notes, when you send a rocket to the moon, you need to know where it is at all times – you can’t just figure it out as you go. In statistics, on the other hand, there are no certainties. It’s about bell curves, random walks, and drawing an often uncomfortable line of best fit between limited data points. Thiel furthermore notes a powerful societal shift towards the belief that statistical thinking ways of thinking will (and should) drive the future.

The example of landing a rocket on the moon is probably no accident. The 1950s and 1960s (coincidentally the first golden age of Silicon Valley) were a time of widespread American optimism. The moon landing was a fundamentally optimistic venture that captured the American imagination (and quite literally would not have happened without calculus). It only makes sense, then, that statistics would be the dominant modality of the cynical world we now inhabit. If you look at the natural disasters, economic collapses, terrorist attacks, and disease outbreaks of the 21st century, some might seem more or less predictable by conventional wisdom, but the popular perception is that humanity was caught napping, apart from a few obscure Cassandras. Especially in light of the truism that we’re usually planning for the crisis that just happened, it’s easy to see the appeal of the indeterminate/statistical model. Statistics couldn’t have predicted exactly which bad things would happen, only that some bad things would happen.

It’s enough to make you throw up your hands, yet this is exactly what Thiel is not arguing for. This should come as no surprise. Thiel is a renowned contrarian, and many of his greatest interests reflect a healthy disregard for statistical/indeterminate thinking, life extension being a prime example. The conclusion of the lecture begins with an acknowledgment that as we embrace the statistical worldview, society is sliding into pessimism, and without indulging in too much pop psychology, it’s easy to see how such thinking becomes self-fulfilling. The lecture ends with an appeal to “definite optimism”, and posits that computer science offers the best hope. CS is not only a great way to solve problems, but as Thiel observes, its fundamental determinism may have something to teach startup culture, which is widely presumed to be governed by indeterminacy.

Of course, software itself is greatly misunderstood, and this is one of the primary challenges computer scientists face as entrepreneurs. People who don’t understand software assume that its value is statistical by nature, and fundamentally unknowable (in contrast to hardware, for example). If you’re a math phobic, single-variable calculus and E = mc2 are just two things you don’t understand, and the differences and relative complexities are immaterial. To make matters worse, people who truly understand software are relatively rare, especially among those with purchasing authority, and this unknowable fallacy leads to a sort of permanent agnosticism in principle as applied to software. Within the statistical frame, it’s assumed that two competing software packages lie in the same general area of the bell curve, and therefore the differences are negligible or at least unknowable. You know that the value of software follows power laws and the differences between good and great are logarithmic, not linear, but the statistical frame ignores all of this.

One consequence is extreme risk aversion: if you believe that the relative merit of one kind of software isn’t calculable, you stick with what you already have, and this has plagued many otherwise forward-thinking institutions. There is also the simple matter of what’s tangible. To the layman, hardware seems straightforward, whereas software doesn’t (even if hardware may owe much of its performance to superior software). As a result, hardware is often seen as a reasonable expenditure, whereas software isn’t. No one blinks at a $50 million aircraft, even if that aircraft is agreed to be 1980s technology, whereas $50 million for software is not only unthinkable to many, but being newer and better may very well work against you, due to the unknowable fallacy.

For the aspiring software entrepreneur, there are a few takeaways. It’s a fact of life that software is misunderstood and undervalued. However, that doesn’t mean quality doesn’t matter. In fact, it matters more than ever. The challenge is that when you are up against a heavy incumbent, it’s not enough to be 10% better – you have to be 10X better, because ultimately your success is dependent on enough people feeling strongly enough about your product to risk rocking the boat. Earlier we discussed that the idea of any complex product being great enough to sell itself is a myth, and again, concluding that being great is unimportant is absolutely the wrong lesson. Put another way, if you want to bring people around to viewing software through a calculus frame, you have to make their daily existence demonstrably better. But wasn’t this always the goal?

This brings up a final point about determinacy: some things are worth doing regardless. In the last CS 183 class, “Stagnation or Singularity?”, Thiel is joined by several guests, including Dr. Aubrey de Grey, gerontology expert and Chief Science Officer at the SENS Foundation. De Grey makes the point that while we may have a fair idea what technologies will be developed, the timeline for development is much more tenuous and subject to various externalities. However, he concludes (paraphrased), “In a sense, none of this matters. The uncertainty of the timeline should not affect prioritization. We should be doing the same things regardless.”

Once again, it all comes down to doing important things, and when this is the stated goal, the inherent pessimism of the statistical approach becomes apparent. This applies to your own life as well as it does when building a company. If you wanted to take the statistical view to its logical extreme and hedge against all possible uncertainties, you’d become a jack of all trades/master of none, and consciously choose not to go long on any particular superpower or world-changing problem. If the goal is to live an inoffensive, comfortable life, this might makes sense. If you want to do anything of lasting value, this is crazy. In some ways, it’s easier to grasp this concept when designing new technology or building a company – although it’s easy to suffer from too many features or too many business models, most entrepreneurs accept that trying to be all things to all people is a recipe for failure (as software development illustrates so neatly). Technology needs a problem to solve. You, on the other hand, are not a problem to be solved – yet what to do with your time and gifts is perhaps the most worthwhile problem of all.

Execution is hard, and distribution is one of the hardest (and not surprisingly, least understood) aspects of execution. Peter Thiel gives the subject of distribution an extremely thorough treatment in CS183 Lecture 9, “If You Build It, Will They Come?”, including mathematics, psychology, and market-specific models. Rather than trying to summarize the extensive substance of the lecture, I’d like to focus on how you might think of the distribution challenge as an engineer, in the context of the Your Future series.

Thiel begins by addressing the most basic question – what is distribution? Surprisingly, many people can’t give you a coherent answer, and if they can, there’s a very good chance they underestimate its importance. If we agree to define distribution as how you get a product out to customers, it becomes a bit more concrete why there’s so much misunderstanding around the topic. It’s especially difficult when you’re creating software or other technologies that require meaningful user engagement. If you think of distribution as just getting a product into users’ hands, you’ll likely fail – either because you assume that a product will get used just by virtue of being available, or that the product will remain in users’ hands once it’s reached them.

If you look at two of Thiel’s biggest success stories to date, PayPal and Facebook, you’ll find two companies that nailed distribution, and in very different ways. It’s worth noting that online payments and social networking sites were both extremely noisy spaces when PayPal and Facebook joined the fray, and neither company had first mover advantage (though as Thiel discusses elsewhere, this may not be such an advantage after all). Also significant is the fact that online payment processing and social networking sites are both fairly easy to prototype and hack away at. Of course both PayPal and Facebook hired outstanding engineers and eventually encountered (and overcame) serious technical hurdles – security/fraud in the case of PayPal and scale in the case of Facebook – but I’d argue that those problems only emerged because they got distribution right first.

As Thiel calls out early in the lecture, engineering bias works against you when it comes to distribution. As engineers, we are conditioned to think that great products will just reach consumers by virtue of being great (and there’s a dangerous tendency to assume that your idea of “great” is representative). The concept of a product being “so good it sells itself” is universally appealing - and universally incorrect. It just doesn’t happen. It is possible to create an environment where the best idea wins within the confines of your own company, and I urge you to retain this form of idealism, but any market is a fundamentally irrational place, and you need to make peace with that fact.

Another major difficulty is that so many young engineers in Silicon Valley have been spoon-fed a massive user base, either because they joined a company that already had one, or they piggybacked on one. Of course, this is a valid distribution channel – the path of least resistance is by no means the wrong approach. The problem is that it skews the way you think about design and innovation. Most engineers in non-entrepreneurial roles haven’t had to think about distribution at all. And that’s fine, as long as you realize that you started on 3rd base and didn’t hit a home run—not for the sake of your ego, but for the sake of your next venture. You have to approach the might distribution challenge with the humility it deserves, so suffer at her hands.

Whether distribution can really be “engineered” is a topic for another day, but it worth thinking about what makes engineering different from sales, and for the aspiring founder, this is one of the biggest takeaways from the lecture. I’m not so much concerned with the merits of different distribution approaches as with recognizing the how the skill of distribution (to include sales) lines up with your and your team’s strengths and weaknesses. It’s no secret that I’m a huge fan of engineering-driven companies, but it’s not enough to focus on your strengths – you also have to even out the competencies you lack, and chances are sales/distribution is among these.

Why is this? As Thiel notes, sales is a fundamentally irrational enterprise, and engineers are concerned with rationality and truth-telling. However, their general discomfort with and lack of aptitude for sales isn’t just about purity of spirit, but also about knowing what to look for. In many cases, it’s not clear what quantifiable skills are actually involved in “sales.” (hint: this ain’t it: Crazy Ernie). If you convince yourself that these skills aren’t important, or don’t have a place in the kind of utopian company you want to create, you not only ignore one of the central aspects of distribution, but also create a huge talent gap, because you need at least a few folks with these skills. Think of it as a special sort of project management skill—the ability to get a distribution project across the finish line. A crude model, but useful in framing the challenge for us engineers.

There are many risks inherent in the worthy goal of starting a company: team risk, innovation risk, technical execution risk, and business execution/distribution risk. In addition to the first three, distribution is something you need to be thinking about in the foundational stage, not something to be revisited at an undefined point in the future. Importantly and subtly, distribution risk affects innovation and technical risk in turn - and every form of risk is ultimately a team risk. Feedback from the field/your customers becomes the fuel (to your creative mind’s spark) for iterating and conquering – you will be on an empty tank without distribution. If you’re trying to start something, it’s almost more important to ask who on your team is credible in each of these areas than how you’ll specifically get there.

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tag:shyamsankar.com,2013:Post/744852012-09-27T14:27:00Z2016-08-13T18:45:40ZThink Again About Sticking Around for a Masters Degree

Mark Twain was said to have remarked “I have never let my schooling interfere with my education”, and whether the quote itself is apocryphal, the sentiment should be applauded. True education is a beautiful thing. A master’s degree, on the other hand, is not only a waste of time (with a few exceptions I’ll get to), but often epitomizes that proverbial interference.

As I spend a lot of time talking to college students I encounter many who are signing up for the increasingly popular one-year master’s degrees. I understand the appeal from the student’s perspective and I understand the appeal from a parent’s perspective. In fact, I pursued one myself. I had just finished undergrad, I didn’t have a compelling opportunity, and more importantly I had somewhere to get to (Silicon Valley). For many of today’s brightest engineers, I don’t think a master’s degree makes any sense, and that was exactly the advice I gave my brother who just graduated. In order to appreciate why a master’s might not be such a wise idea after all, it’s worth considering what makes education meaningful to begin with.

To begin with, education creates opportunity. This has probably been drilled into your head from an early age, and for good reason. If your parents were the first generation in your family to attend college (or you are) this needs no explanation, and it’s a tragedy that education is taken for granted by anyone, let alone so many.

There is also much to learn – much more than most people would ever guess. When I went off to college, I certainly thought I knew more than I did. Being disabused of this notion may seem like the first step in one’s education, and it often is, but it’s a really a lesson worth relearning at any stage. Then there’s the process of learning how to learn, and this is one of the primary reasons you go to school, independent of your field of study. There are countless dimensions: learning to make abstractions and conceptualize, to interrogate a problem, to work inductively and deductively, to separate first principles from careless assumptions. You need to experience breadth, both to strengthen your foundation, and to find subjects worthy of exploring in depth.

Education also provides a unique platform to gain impactful life experience in a low-risk environment. School is a place to build formative relationships, explore different paths, be in charge of your own time and activities, even start something if you are so inclined. Perhaps most importantly, it’s a time to learn about your strengths and weaknesses with high upside and low downside. Of course, college is costly, and time itself is far from trivial, but it’s much easier to avoid loss aversion and do something truly experimental when you’re not deep into your career. The phrase “do something” is the key here – “finding yourself” has become sort of a cliché of indolence, but it’s while moving forward that you truly find yourself, and college can be the perfect place to do this.

Finally, education validates that your best years really are ahead of you. High school is certainly a valuable experience, and in the best case can lay the groundwork for the level of exploration that college makes possible. At the same time, it’s a small pond both socially and in terms of what you’re asked to do. However triumphant or painful it is, it’s not a place to remain. College may feel like a time for reinvention, but it’s really a time for original discovery.

To understand why master’s degrees are superfluous and even counterproductive for engineering students, I like to use the framework of getting somewhere versus getting something. You can also think of this as a means to an end as opposed to an end in itself. Education provides many things of intrinsic value, but much as nine months, give or take, is enough to prepare you for the outside world, so is one degree.

The exceptions tend to fall into the category of getting somewhere: for example to Silicon Valley or to the United States. A master’s degree can also be a good way to test the waters of academia. You can take classes with doctoral students and get a feel for the academic life without having to commit to a dissertation or taking on a teaching schedule. For some friends, a master’s has been an informative gateway to a promising academic career, whereas others consider it worth the price of admission to have been persuaded that doctoral studies aren’t for them after only a year. I am not coming down on one side or the other, only advocating for informed choices.

On the other hand, if you’re trying to obtain something – experience, distinction, deeper cultivation of your superpower – it’s usually better to just get that thing in its pure form. If you want entrepreneurial experience, go out and get some – don’t learn about it in an MBA course. If you want to be a better software engineer, don’t sign on for an extra year of TAing - work on challenging, real-world problems in a production environment, with peers who force you to raise your game.

I’ve said before that learning for its own sake is not necessarily valuable, and this is especially true of master’s degrees in the workplace, especially degrees in one pure subject (as opposed to MBAs and other first professional degrees). There is a lot of misleading and unsubstantiated chatter that a master’s degree makes you a more valuable employee ipso facto, and this is just not the case, as many people find out the hard way.

It’s also worth acknowledging that there is often a socio-cultural bias towards more education, especially among generations who experienced firsthand the power of education to achieve an objectively better life. This is not a perspective to be discounted, but at the same time you need to recognize when the preference for more higher education is no longer contextualized and ignores the question of somewhere versus something.

Finally, and most importantly, don’t do a master’s because college is fun and it will never get better than this. If you care about your future as much as I imagine you do, that simply won’t be true (regardless of whatever sentimental projections people offer you). The fifth year isn’t like the fourth. Everyone is gone, and you realize that what made college meaningful was the people who went through the experience with you, not the buildings and the campus. Moving on is not always easy, especially when you strip away the structure and predictability of school, but it’s simply time to forge new experiences. If there’s one thing I’ve learned since leaving school, it’s that they can all be the best years of your life when you get out there and Do Important Things.

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tag:shyamsankar.com,2013:Post/744882012-08-21T21:34:47Z2013-10-08T15:37:34ZDavid is not Goliath, and should not be

When you are doing something truly disruptive, you are in a David versus Goliath situation (and this is especially true for technology). The story of David is highly instructive for anyone who aspires to do world-changing things, and its lessons go much deeper than an inspirational tale of the little guy beating the big guy.

Let’s begin with the obvious: David wins by not playing by Goliath's rules. He doesn't out-muscle Goliath, instead fighting a lightweight, guerrilla style insurgency. David is Exhibit A for the theory that speed, wits, and the ability to adapt can trump size, resources, and heavy armament. After felling Goliath with his slingshot, he beheads him with his own massive sword (a gory but potent bit of symbolism often left out of the retelling).

However, David’s selection as champion of the Israelites and his rise to field commander were unconventional, even revolutionary acts in themselves. In fact, almost every key event of David’s ascendancy was highly unlikely. It began when the prophet Samuel sought out Jesse of Bethlehem, believing that one of his sons would become king of the Israelites. Samuel rejected each of Jesse’s grown sons in turn before Jesse reluctantly presented David, his youngest son and a mere shepherd. Anointed by Samuel, David went to the court of King Saul, initially as his armor-bearer. Yet it was as a musician that David made himself indispensable to Saul, healing his afflictions with his sublime harping. When war broke out with the Philistines, David was not even asked to fight at first, instead going home to tend his father’s sheep. When David arrived at the front to answer the call, he faced fierce opposition from within the Israelite ranks, chiefly from his own brothers. I suspect that when Saul, not renowned for his piety, gave David permission to face Goliath, he was not 100% faithful, but instead thinking “this is so crazy it just might work.”

After numerous trials, including his betrayal by Saul, David was crowned King David. He ruled unconventionally and brilliantly, true to his essence, and in doing so established the House of David and the true throne of Israel. Famed as a warrior, he never forgot that he was also an artist, and crafted psalms as powerful in their own way as his armies. This is not to say that David’s reign was a wholly peaceful one, or that his better judgment always prevailed. He made his share of prideful mistakes, and suffered no shortage of tragedies, none more painful than the deaths of two of his sons. However, David proved willing to build on his failings, and never stopped bucking convention. When the time came to choose a successor, he passed over his heir apparent for Solomon (originally the product of adultery with the wife of one of David’s commanders). Solomon, of course, built the great temple of Jerusalem, composed the Song of Songs, and became synonymous with surpassing wisdom. Ultimately, the line of David exemplifies the divine ascendancy of the unlikely.

For technology entrepreneurs, the story of David is a highly attractive one, and the modern-day parallels are striking. You can think of David’s slingshot as one of the original disruptive technologies – it’s lightweight, requires minimal training, and utilizes off-the-ground commodity hardware. It is likewise fitting that the term “Philistine” has come to mean someone without any appreciation for art and learning, and this is especially true concerning the perception of software, perhaps the most misunderstood and underappreciated form of technology at the institutional level. Of course, David himself is the most inspiring part of the story, a young, fearless, brash, but supremely talented leader who emerges from the least likely of places with the most counterintuitive blend of skills.

However, those who would follow in David’s footsteps must beware the catastrophic, yet often subtle pitfalls along the path. It is paramount that as David wins, he doesn't become Goliath. For leaders who emerge from the tornado of the hyper-growth phase, this is deceptively easy to do, and the annals of technology are piled with the cautionary examples of companies born from innovation that faded into irrelevance by allowing themselves to become the hated establishment. David must be true to who he is, not by consciously choosing to remain small and irrelevant, but by resisting Goliath’s arrogance and vulnerabilities - even while embracing growth and influence.

I spend a lot of time working with large and important institutions to help them solve their biggest problems. This tremendously rewarding, and as the sense of partnership and investment in their mission develops, it is tempting to want to be of them as well as work with them. Yet you can only help them if you are true to David, and this requires you to maintain the unique identity and vantage point of the constant outsider. And this is why massive institutions need the help of entrepreneurs, even if they don’t realize it at first. This is inevitably a bumpy process, because the cultural bias is to keep David in a limited role, away from the front. Eventually, though, it becomes clear that in order to do radically different things, they need radically different competencies and perspectives. If it was simply a matter of finding better top-down management, they could promote from within. To enlist a warrior psalmist is a different thing entirely.

Of course, embracing unconventional wisdom is only the first step. The far greater hurdle is how to institutionalize agile and independent thinking without becoming doctrinaire and inflexible about it – an ironic but all too common mistake. Interestingly, this applies to both the century-old brand name that seeks to embrace entrepreneurial culture and the scrappy startup that suddenly finds itself with thousands of employees. Once again, David rides to the rescue. Consider the fundamental challenge faced by US Special Operations Command, a four-star headquarters with almost 60,000 personnel, charged with maintaining supremacy in lightweight, unconventional warfare. Former commander General Bryan Brown, who enlisted as an infantry private and retired as one of the great visionaries of special operations, once remarked that USSOCOM needs its poets too. David knew it all along.

In the light of delivering outcomes, we should consider the respective merits of horizontal and vertical approaches. The more common (and not necessarily incorrect) approach is to slice a problem horizontally and build layers to a stack based on objectives with clear left and right parameters. This not meant to imply that there is a direct correlation between inefficient, services-based businesses and horizontal integration – indeed, many companies could (and do) achieve considerable efficiency through sophisticated slicing of problems and applying focus and discipline to a specific layer.

However, this scenario presupposes that horizontal slicing actually makes sense for the problem at hand. More often than not, this won’t work for truly world-changing problems – thorny, costly messes that have always defied incremental or partial solutions. For these kinds of problems, you generally need a vertical slicing methodology, wherein the problem writ large is solved end to end.

Complex problems are a lot like the classic peg-and-hammer game: knock one peg down and another one almost instantly pops up. Focusing on one problem may cause another one to become more pronounced, or it may result in an entirely new problem emerging (the law of unintended consequences). Once you begin to recognize that the underlying structure defies incremental approaches, the revolutionary no longer seems implausible – in fact, it often proves to be essential.

One aspect that makes vertical approaches so hard is the amount of abstraction required. Many business leaders pride themselves on seeing the bigger picture, but paradoxically, successful abstraction requires a concurrent grasp of concrete problems throughout the stack. You can code in Java or C, but if you want to truly push the boundaries of performance, you need to understand what the computer is doing at the CPU level. Conversely, nuts-and-bolts problem solving does not amount to a transformative business without some broader vision, and this requires abstraction. In the vertical methodology, there are two main tiers of abstraction: that required to solve a specific problem end to end, and that required to generalize this solution to whole classes of problems. Finally, it is worth noting that the abstraction challenge is equally crucial to management as well as technology, and morale tends to be worst when people can’t make meaningful abstractions – or feel they can’t.

Apple is a great example of the success of the vertical approach, not only for its mastery of end to end considerations, but for rethinking the whole model of personal computing – a deft blend of granular and abstract problem solving. In contrast to the Microsoft approach of focusing on operating systems and desktop software and letting hardware manufacturers fight it out, Apple chose to take responsibility for the entire experience, from the hardware to the user. This required not only solving all the normal granular problems associated with each layer of the hardware, OS, and software stacks, but actually creating a continuous whole. It’s about more than just a pleasing design – the idea that the user interface encompasses the device itself is central to Apple’s identity. For Apple, the medium is the message, in three dimensions. By slicing multiple user needs vertically, Apple ended up creating a whole new vertical, one it continues to dominate.

Where most companies saw problems they’d rather not touch, Apple saw opportunities – in device design, in platforms, and in challenging a monolithic company that conventional wisdom would suggest was unbeatable. For years, Microsoft succeeded because they had created the most effective walled garden. Although Apple’s OS had always had loyal adherents, Microsoft’s wall arguably started to erode with the introduction of the iMac and Macbook, which caused consumers to reconsider the fundamental relationship between form and function. With the creation of the iOS developer platform and app store, the walled garden concept was turned inside out. Yet I would argue that the platform, for all its merits, would have been exponentially less attractive if people were not already loyal to the device and the entire experience it represents – a shining validation of the power of vertical problem solving.

There is one quite interesting twist to all of the above: in the course of developing a vertical solution so you will actually develop strong intuition about how the problem can be sliced horizontally. This is the entropy of the human approach and the way we naturally organize to solve problems in teams – i.e. frontend and backend, to use a very broad example. Within each category, you can further refine the dynamics of the cross-functional teams that enable each horizontal slice.

As a final note, it’s worth mentioning that while the vertical approach at its most successful may create the impression of an all-encompassing technology, an end-to-end solution almost is achieved through scrupulous attention to each layer of the stack. However unified the whole may appear, the parts each play a discrete and necessary, not sufficient, role. Developing vertical solutions is not a matter of finding a silver bullet, but rather the most effective combinations and permutations of thousands of lead ones.

The remarkable ascendancy of China and India as high-tech powerhouses has illuminated a non-obvious but profound truth: The United States remains the best place in the world for high-end software development. Despite widespread economic pessimism and rumors of a global power shift, talk of across the board decline is ill-founded. When it comes to software, American exceptionalism is alive and well. The first secret to our dominance is our engineers. To anyone familiar with the basic mechanisms of software development, this is really no secret at all. What is more surprising is how much our engineering culture owes to the heartland. While our software industry may be concentrated among the coastal elites, the ethos that makes it possible was largely forged in the wheat fields and corn rows of the Midwest. Properly applied, it is this ethos that will see us through current uncertainties and solidify our place at the top for generations to come.

“Global” does not equate to “world-class”, and there is no better illustration of this than the booming software industries of China and India. This is a period of unprecedented growth and opportunity - certainly compared to the landscape my own father faced when he left Tamil Nadu a generation ago. At the same time, per-capita income in India is less than half of China’s, itself a seventh of that enjoyed in the United States. Yet income disparity is merely a symptom of a larger trend: for all the high-tech jobs created in the past decade, China, India (and the rest of the developing world, for that matter) have yet to produce their own Microsoft, Oracle, or Google. Instead, “body shops” offering outsourced IT services and low-end development are the rule. On the value spectrum, such businesses are the equivalent of the call centers that now symbolize the tremendous growth and equally tremendous limitations of the new global economy.

When software development is a process of manufacturing, not invention, the resulting products are commodities, and the same holds true for software engineers. This is the crux of the issue: great software is not a commodity, but a highly evolutionary thing, and great engineers are irreplaceable, not interchangeable. The relative talents of software engineers, and the elegance of their output, lie on an exponential, not linear, scale. The difference between the very top and the median is not 30%, not 300%, but rather 10,000%. Of course, this phenomenon is not only illustrated by the IT factories of Bangalore and Shanghai. Plenty of American tech companies persist in believing that an arbitrary number of decent software engineers equals one savant.

However, the creation of a high-end software industry requires more than just top technical talent. In the absence of the right environment, innately gifted engineers will either flee or toil in obscurity. This environment must be supportive of personal aspirations, while remaining a firm meritocracy in which the best idea wins. A sense of fairness must prevail, especially as it relates to competitive marketplaces, respect for intellectual property, and recognizing top individual contributors while promoting a spirit of teamwork and cooperation. Most importantly, new talent is attracted to this environment by the promise of just opportunity, not a pre-ordained lot in life. These are the essential Midwestern values that drew the first homesteaders inland from the American colonies, sustained a young Thomas Edison through countless early failures, and, in our own century, have given rise to the greatest icons of the knowledge economy.

All of this helps to explain why, despite tremendous advances in technical education throughout Asia (and comparative stagnation in our own system), the United States continues to be the epicenter of world-class software development. This is not meant to suggest that the recent educational achievements of Asia are anything less than remarkable. However, the best measure of these achievements is not the number (or lack) of new Googles springing up across the Pacific, but rather the influx of highly qualified Asian engineers and students seeking opportunity in top American companies and universities. China and India have succeeded admirably in creating cultures of productivity, yet a culture of invention remains elusive. The upshot, for American software leaders, is a workforce that is much more diverse than it is often gets credit for. Ironically, it is the native Midwesterner that is often absent from the popular perception of what a software company looks like – but we should remember that the first Midwestern settlers were primarily immigrants themselves.

While panic and pessimism are not appropriate reactions to our changing world, neither is taking our dominance and innovation for granted. Instead, we should consider what our values have to teach us in light of certain austerity. Our forebears, facing similar challenges, learned to do more with less. Happily, this concept applies better to software than to almost anything else. Moore’s Law states that computing power doubles roughly every two years. Given the ingenuity of our software developers, we should absolutely demand twice the performance for half the cost when it comes to IT infrastructure for healthcare, defense, treasury, and other mission-critical departments. It is not enough to slash underperforming programs (and they are legion) without making concurrent investments in world-class technology. This may seem counter-intuitive given our shrinking budgets, but we can only achieve the efficiency and productivity gains required by thinking in terms of value as well as cost. We must reward our top producers, but only while demanding their very best efforts.

The unique software engineering culture of Silicon Valley may simply be the most recent manifestation of America’s pioneer values. However, it is these enduring values, more than any technical achievement, which will ensure America’s continuing dominance of the software world in a time of global change.

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tag:shyamsankar.com,2013:Post/744942012-07-14T19:00:00Z2016-09-13T22:34:22ZTime For a New Type of Product Company

Even a cursory glance at the worlds of technology and services (and the murky realm that lies between them) makes one thing clear: it is time for a new type of product company.

What’s wrong with the old one? Take enterprise resource planning as an example. ERP/supply chain management was once a world-changing problem, yet even after decades in which to evolve, the typical ERP system solution still costs tens or hundreds of millions of dollars and requires substantial time to implement - sometimes as long as or longer than the first versions. Why, then, does this model of “solution” persist so stubbornly?

One explanation is outdated reference points. The previous generation of product companies grew up in a world in which bespoke software was the only viable option. To solve a problem by conventional standards, they either had to build everything from scratch, or glue a bunch of existing products together into an uneasy coexistence. Given the obvious shortcomings of the latter approach, the former takes hold in many cases.

Of course, the bespoke approach has numerous shortcomings of its own. It is largely inefficient, and discounts the extent to which large classes of solutions can be productized. Interoperability with existing systems and future products invariably suffers. Many customers will commission products that should be reusable elsewhere, yet impose IP restrictions that prevent wider application. The largest companies may succeed in creating a continuum of seemingly complementary offerings (end-user software, administrative software, databases, mainframes, hardware, etc) - yet behind the kimono they are actually services businesses. Each additional piece of hardware or software exists to sell more services, and in doing so, consolidate ownership of customer environments.

However, the deeper (and much more destructive) phenomenon at work here is the matter of structural incentives. From the vendor’s perspective, the bespoke development model usually means each product has been developed on a specific, proprietary customer’s dime (indeed, it is hard to imagine a purely bespoke model working without rigid exclusivity clauses). The bespoke developer bills for the engineers’ hours, not the final product, and inevitably requires a legion of consultants to make the product work and provide further customization ad infinitum. Conveniently, the longer the project takes to complete, the more money the company makes. Even if there is no gross corruption, there is no incentive on the margin to make anything more efficient.

On the customer side, structural incentives should be better aligned, but loss aversion tends to create resistance to change, especially in big companies. The larger an organization’s technology purchasing infrastructure (and the more layers between it and the end-user), the more the incentive structure is influenced by the practical need to defend oneself to the outside world. Because appearance is everything, the relative downside of being associated with an unsuccessful technology acquisition is fairly high, while the relative upside of a successful new implementation is surprisingly low. As a result, “safe” decisions quite often win out over the best decisions, which are usually non-linear and difficult for everyone to easily recognize, even with the purest of motivations. The effects of loss aversion reverberate far beyond technical functionality – they limit the vision of the whole organization, even among those who ordinarily might embrace innovation. As consumers and as a society, we have not been as perceptive as we probably ought to be about the efficacy of the traditional approach to building products - if we were, we would be more willing to learn and iterate with a long-term outlook.

Left alone, the traditional approach, for all its faults, seems destined to persevere. Hence, I maintain that we need a new type of product company: one that actively takes responsibility for the outcome the product is supposed to deliver. This may sound intuitive enough, but it actually represents a revolutionary departure from traditional companies that deliver technologies or capabilities at best. This company must have both the skills to productize new technologies and the incentive to continue iterating until its products work out of the box (and working out of the box must sooner or later be seen as a firm requirement, not a nice-to-have). The tricky part, however, is that this incentive must be created alongside the product itself.

Let’s first consider the incentives driving the traditional model. Creeping development schedules and requirements reliably create larger and broader revenue opportunities. This results in an obvious disincentive to stay on schedule and under budget, but it is a commonly accepted model that many large customers are well-prepared to accommodate. Increased dependency on services creates more revenue opportunities, and has the additional benefit of locking in customers, who may not have the desire to administer their own technical systems (or even believe it to be possible). Finally, a subtle but profound incentive is that companies are rewarded for convincing customers that each of their problems is a unique snowflake and requires an equally unique solution, built from the ground up, at bespoke speeds.

The new model is a study in contrasts. There is no incentive to delay, partly because the company does not rent labor by the hour, but largely because working out of the box is a major part of the value proposition. The outcome-based model prizes efficiency for both customer and vendor, and as a result, the focus is on solving immediate problems with the subsequent goal of generalizing and productizing solutions to whole classes of problems. However, the desired outcome must be achieved without compromise, so there is also a strong incentive to create a product that is adaptable to very specific requirements. The overriding incentive, however, is to do something truly extraordinary, and realize substantially more value based on a substantially better outcome. If you can save a customer $1 billion, it is not unreasonable to charge $250 million for the privilege. By the same token, a solution that is duct-taped together over months and years and thrown over a wall, leaving the customer to struggle to realize a fraction of the savings themselves, should be rewarded proportionally.

This is the big bet driving the outcome concept: outsized rewards for outsized (and demonstrable) value. Of course, it will absolutely never happen this way overnight. No doubt the outcome model is much riskier business in the short term. The company must assume the risk of development costs, since the new model requires them to create value first before they can charge for it. Delivering a meaningful outcome also requires top engineering talent, which cannot simply be purchased, and certainly not as quickly as one would like. Finally, lest we forget, the structural incentives to avoid change are alive and well. In order to have any chance at all of making it, the entrepreneur must take on the lion’s share of the risk, betting that a fair price for a guaranteed outcome will be a lagging indicator in the best case scenario. In the short term, true outcomes will almost inevitably be undervalued, but the reality is that the burden of proof is on the innovator. (To be fair, there is risk for the customer as well, even if their initial monetary outlay is low. Ideal outcomes are not usually achieved all at once – early on, they will require the customer to invest their time, provide access to their hardest problems, and be willing to iterate without real certainty that the new company will necessarily succeed where previous vendors fell short).

A business model based purely on delivering outcomes would amount to a seismic shift – a wholesale reinvention of what it means to be a product company. So be it. This is unquestionably a difficult road, but if the past has taught us anything, it is that the only way for entrepreneurs to achieve the changes they desire is to see them through from inspiration to outcome – as high-tech companies everywhere like to say, end-to-end. More often than not, this means solving problems (and even conceptualizing them) vertically rather than horizontally. A truly vertical approach represents a major distinction and radical departure from the norm, and will be covered in depth in the next installment.

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tag:shyamsankar.com,2013:Post/744962012-07-14T02:37:00Z2013-10-08T15:37:34ZThe Difference Between High Achievers and Leaders

Those selected for development have one universal trait in common: They are by definition high achievers. But there is a difference between those superstar achievers that can make the leap to CEO and those that will implode: To what degree do they feel invigorated by the success and talent of others, and to what degree does the success of others cause an involuntary pinch of insecurity about their own personal inadequacies? Only an individual who feels genuinely invigorated by the growth, development, and success of others can become an effective leader of an enterprise. And it remains the most common obstacle of success for those trying to make that leap.

Doing what you’re good at is an obvious prescription – perhaps a little too obvious for the aspiring entrepreneur who most likely takes pride in discovering the hidden dimensions of everything! It can’t be that simple, can it? I would argue that it really is simple - just not easy. Discovering what you’re good at takes time, intellectual honesty, and an even greater awareness of what you’re not so good at. Usually, some failure is required. It’s also not a binary system – not everything fits into neat categories of what you should and shouldn’t be doing, and as discussed, starting something substantive inevitably requires you to do a little bit of everything. That said, ascending to the next level will require you to leverage your strengths as never before.

You have certain obvious strengths you already know about. Getting into (and through) college requires a broad range of competencies that we often take for granted, and it’s easy to confuse competencies with real strengths, especially if you have the kind of work ethic that can compensate for subtle weaknesses. Even so, you probably have a good idea where your major strengths lie, and they can all serve as a clue as to what you should be doing with your life.

Within these strengths there is something you are so good at that it seems effortless – and because it’s so intuitive, you may assume others can do it too. This sometimes manifests itself when working in teams - when something comes so easily it can be hard to appreciate how it could be a real struggle for someone else. As a result, you may even undervalue this strength, which would be a shame, because this is your superpower - the key to making your greatest impact. The common factor among every great entrepreneur I know is having a superpower and knowing how to use it (despite often being below average in many other facets). “Well-balanced” individuals, by contrast, tend to be a hit at management consulting firms and other places where job titles actually include the word “generalist”.

Then, there are the things you aren’t good at, but would really like to be. The problem is that early on, you define yourself in part by being good at these things, and this can be really hard on the ego. It is a massive distraction from your true strength. But it is really important to emotionally and intellectually learn to let go here. And context matters as well – you might be perfectly adequate at one of these aspirational strengths in most people’s eyes, yet exist in an arena where you need to be among the best. I eventually had to accept that I would never be the greatest programmer (as much as I wish that weren’t true!). I could have continued to program for a living, but not if I wanted to someday work with the top software developers in the world (as I now do). I don’t regret having tried, but really this was just part of my journey to really understanding my strengths.

This is not to say that struggle isn’t valuable, but as with learning, people overestimate the value of struggle for its own sake. It’s largely a matter of recognizing what’s worth struggling for and what isn’t. Achieving your maximum impact isn’t just about identifying your talent and riding it to greatness. Almost everyone has weaknesses that blunt the impact of their strengths, and while these weaknesses might never be banished, you can absolutely learn to control them.

The final thing to remember is that this is largely a process of self-discovery. Your mentors and colleagues can help you get there, and they will definitely have insights that only an outside vantage point can provide. Ultimately, however, no one can do it for you – and that realization should excite and inspire you even more.

There is something uniquely valuable and satisfying about starting something while you’re still in school. The iconography of Silicon Valley is certainly rich with companies founded by students, Google and Yahoo being prime examples. Having started a company as an undergraduate myself, I am a firm believer that it’s one of the most valuable things you can do –not because you will create the next Google, but because you will learn a great deal of things worth learning.

Of course, starting something does not have to mean starting a company – the experience of leading a substantive effort from scratch is the irreplaceable piece here. It is all about the creativity and execution required to breath something to life. The good news is that universities can be great places to do this. If you know where to look, you will almost definitely find like-minded students and supportive faculty. You don’t necessarily have to create all the infrastructure yourself, either – there are many competitions and programs designed specifically to promote student inventions, such as the DARPA Grand Challenge, a driverless vehicle competition. Starting an organization can be a great way to attract like-minded people to your goals, and catalyze further invention and innovation through the network effect. A great example is Cornell University Sustainable Design, founded by Sam Sinensky as an offshoot of Cornell’s annual Solar Decathlon team and has since given rise to many exciting initiatives.

I took a leave of absence from Cornell to start my company, and I will be the first to tell you it didn’t actually work. It was, however, an awesome experience, and not only in the sense that failure is a great teacher. I’ve always felt that the best way to learn things is by doing, and win or lose, starting a company involves plenty of doing if you are serious about it. As Herb Kelleher, co-founder of Southwest Airlines, famously quipped, “we have a strategic plan – it’s called doing things.”

The beauty of founding or co-founding something is that you do literally everything. Some aspects of this are highly educational – getting to a working prototype, romancing prospective customers, fine-tuning your business model, and seeking financing are all subjects worthy of great minds. Other aspects, such as paperwork, taxes, and yes, taking out the trash, merely build character. However, character is something you will need a lot of, and here’s why: execution is substantially harder than people think.

To begin with, executing on anything at all is a radical departure from the comfort zone of toying with ideas, during which success seems assured and all sacrifice is theoretical. Furthermore, most people severely underestimate the difficulty of simply owning your own schedule – there is no magic checklist for what constitutes a productive day, and you have to be able to forge ahead without constantly worrying if you’re doing it all wrong. Starting a business, in particular, requires you to make judgment calls with real implications – who to align yourself with, how much funding you’ll need, and perhaps most significantly, who else should join your team. Finally, you will have to execute in conditions of great uncertainty, without worrying that your projections are disintegrating before your eyes. There is never enough information or unit testing to be comfortable, yet you also know that at a certain point, any more time spent gathering data or polishing your prototype will cost you momentum you may never get back. Sooner or later, you have to do it live.

Starting something in school will teach you a lot about how the world really works, but it will also teach you a lot about something you probably thought you knew pretty well: yourself. It will test you in a way that academics fundamentally can’t, and people invariably underestimate the struggle involved. This is not just a function of iffy externalities, but also your own strengths and weaknesses. Even if you have a sophisticated understanding of these (and few undergraduates really do – I know I didn’t and am still refining it), there is a tendency to believe that being a founder will hone your strengths and bolster your weaknesses in equal measure. This sounds perfect in theory, but it’s simply not realistic – nor is it a recipe for doing truly important things. In the next installment, I’ll be exploring why you must do something you’re great at before you can do something great.

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tag:shyamsankar.com,2013:Post/745022012-04-29T01:57:43Z2014-11-28T07:26:41ZHead West

One of the main emphases of the Your Future series is putting yourself in the best place to succeed. You know by now that for me, this means Silicon Valley, but some of the particulars might surprise you. My path here has given me a profound attachment to this wonderland of innovation, yet it has also deepened my engagement with the world at large in ways I never could have imagined. The Valley has taken me, as Bilbo Baggins might have put it, there and back again.

Silicon Valley rocks…enough said? Let’s start with the obvious: Silicon Valley is the premier engineering area, and has been for longer than you might think. If you want to change the world, you want to be among the best in the world, and no one even disputes that Silicon Valley is home to the top technical talent anywhere. Likewise, as an aspiring difference-maker, the atmosphere of excellence should already excite you – whether your ultimate destination is Silicon Valley or elsewhere, it’s safe to assume you are not content with being a big fish in a small pond. Living and working among the best poses numerous advantages and creates real opportunities – these are just a few that come to mind:

It improves your game. I’ve always felt that Silicon Valley is unique in that your peer group extends far beyond your immediate colleagues, and the opportunity to interact with and learn from the best is unparalleled here. Attend a SuperHappyDevHouse or Hacker Dojo event and you will see what I mean.

Silicon Valley is a place to get noticed. The network effect is hard to overestimate, and Silicon Valley employers take a much more meritocratic and imaginative approach to recruitment than traditional industry. More than anywhere else, ability trumps experience, and the operative question when considering young talent is usually not “why”, but “why not?”

Silicon Valley is a place to find the human catalysts for realizing your goals. You can think of this not only in terms of employers and colleagues, but also co-founders, first hires, and mentors and advisors.

The access to capital is unmatched. There are half a dozen cafes on University Avenue where you can’t order an espresso without rubbing shoulders with a top-tier venture capitalist or angel investor, and chances are they want to meet you as keenly as you want to meet them.

My Story: After family stints in Mumbai, Nigeria, and Orlando, I headed to Cornell. I received a fantastic technical education, and this laid the groundwork – without a technical foundation, nothing that followed would have been possible. Coming out of Cornell, New York and Washington, DC are the logical centers of mass. I love the energy of New York, but the downside is that the top technical people gravitate to finance, not engineering – or if they remain engineers, they spend their time building robots for small groups within an investment bank. DC does a great job of attracting talented people with an ethic of service, but it can be a very difficult place to do anything fundamentally disruptive. Even worse, New York and DC don’t have a culture that treats engineers as first-class citizens, and if this is true in such vibrant cities, you can imagine how it is elsewhere. Fortunately, this is starting to change – for example, I am watching Cornell’s new campus on Roosevelt Island with great excitement, and I’ve had a great time and met great people at Digital Capital Week.

As much as I enjoyed my time in Ithaca, there wasn’t much startup culture to speak of – at least not then. For the top computer science students, Microsoft was a dream job, maybe Google if you were exceptionally adventurous. There was no model for going to a startup after graduation, let alone going to school primarily as a means to immediately enter the entrepreneurial fray. And this was exactly why I headed West, to Stanford, for graduate school. It may sound crazy, but the degree actually was just a vehicle to become a part of the community.

The plan worked. After just a few months, I became employee #5 at a startup, and spent the next two and a half years traveling throughout Latin America, Africa, India, and Southeast Asia building an online-to-offline payment processing network. I may not have gotten rich, but I was given a ton of freedom (and responsibility), learned a great deal, and was exposed to a whole new universe of entrepreneurs, investors, and technology pioneers, forging relationships that will outlast any job. When the time came to move on, I had a cornucopia of emerging companies to pick from, and enough insight into my own strengths, weaknesses, and passions to make an inspired choice.

Beware the Bubble: Silicon Valley unquestionably rocks, but as with any truly special environment, it can be too insular at times. Of course, many entrepreneurs come here for exactly this reason – there are few distractions from the overarching goal of building something great. That said, your awareness of what actually constitutes greatness can fossilize if it’s not challenged. The spirit of innovation and improvement is not a sure immunization against group-think and self-congratulation, and even the most gifted people can fall prey to the sense that the rules of the road don’t apply to Silicon Valley. However, it is still the epicenter of entrepreneurship and engineering culture, and the good easily dwarfs the bad. The best antidote to insularity is an infusion of fresh talent and fresh perspectives, and this is where you come in. Realistically, it is much easier to make your mark in Silicon Valley than to try to replicate it elsewhere. Changing the world requires laser focus, and creating the infrastructure and pre-conditions for success from scratch will inevitably detract from that focus.

More importantly, the rest of the world needs Silicon Valley more than ever. For too many companies and individuals, the model of success never requires them to step outside the Valley. Yet, technology is an amazing lever for change, and in my experience the best way to fight insularity is to engage with broader scales of challenges. My first job out of college took me to the world’s great cities and mud-walled villages alike, and I’ve been fortunate to continue venturing outward ever since. I’m a firm believer that to change the world, you must first see the world, and Silicon Valley can be a tremendous catalyst for both.

Peace, Love, and Understanding: Expanding your way of thinking is essential, yet there is also something to be said for being around people who not only share your interests and passions, but also understand you on a fundamental level. As motivating as it is to be told you’re crazy, it’s even better to find kindred spirits to join in the effort. Silicon Valley is above all a community of passion, and it is difficult to convey the motivational power of such a community without experiencing it firsthand.

We’ve all heard some variation on this maxim: “Find a job you love and you’ll never have to work a day in your life.” I appreciate the sentiment, but I don’t completely agree. As much as I’ve enjoyed my time in Silicon Valley, I’ve never felt like I wasn’t working – for myself, for my teammates, and for a common goal. And because I’ve always been working for something, I tend to think that work/life balance is a false dichotomy, and so I’ve made it a practice to seek out individuals who felt the same way. Whether there’s a business opportunity or not, good things happen when people who share defining values find each other.

In February of 2010, I had coffee with a computer scientist-turned-entrepreneur who had created a compelling prototype for a new educational technology, and was looking for office space and strategic advice. Although our respective markets were worlds apart at first glance, I was captivated by how similarly we approached our work and how much we defined ourselves by our passions. A few months later, we were engaged, and exactly one year from our second date, we got married, surrounded by many of the close friends we had made on each of our entrepreneurial journeys. After a two-day honeymoon, she was off to a conference and I was off to visit my biggest international customer. Since then, it’s been an incredible ride. It hasn’t always been easy, but Pooja and I both came to Silicon Valley knowing we’d have to make an extra effort to live our dreams – all of them.

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tag:shyamsankar.com,2013:Post/745042012-04-14T01:40:00Z2013-10-08T15:37:34ZHow to Get a Job

Illustration by Neal FoxCompanies typically look for well-rounded people. They want an A-plus in every category. We tend to think it’s better to have an A-triple-plus in one area, which presupposes an F in other areas. So maybe we end up with someone who solves problems very creatively but can’t interact with people. We look for people within uneven IQs, then we build a role around their strengths. I like to meet candidates with no data about them: no résumé, no preliminary discussions or job description, just the candidate and me in a room. I ask a fairly random question, one that is orthogonal to anything they would be doing at Palantir. I then watch how they disaggregate the question, if they appreciate how many different ways there are to see the same thing. I like to keep interviews short, about 10 minutes. Otherwise, people move into their learned responses and you don’t get a sense of who they really are.Karp is CEO of Palantir Technologies, which mines data for intelligence and law enforcement communities.— As told to Ashlee Vance